{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Putting Some Pandas In Your Python 🐼**\n",
    "\n",
    "<img style=\"float: right;\" width=\"400\" height=\"400\" src=\"image/00_pandas.jpg\">\n",
    "\n",
    "### **What's covered in this notebook?**\n",
    "1. Introduction to Pandas\n",
    "    - What is Pandas?\n",
    "    - What kind of data does Pandas handle?\n",
    "    - What are the Data Structures in Pandas?\n",
    "    - How do I read and write tabular data?\n",
    "    - Installation\n",
    "    - Importing Pandas Module\n",
    "2. Series\n",
    "\t- Creating Series using Python list or dict\n",
    "\t- Creating Series from Numpy ndarray\n",
    "\t- Creating Series from scalar\n",
    "\t- Accessing Properties/Attributes and Methods of Series\n",
    "\t- Accessing data using Indexing and Slicing (Read Operation)\n",
    "3. DataFrame\n",
    "\t- Creating DataFrame using Python dict, list or tuple\n",
    "\t- Creating DataFrame using Numpy Array\n",
    "\t- Accessing Attributes/Properties and Methods of DataFrame\n",
    "4. Working with Tabular Data\n",
    "\t- Dataframe to .csv & .xlsx\n",
    "\t- Reading .xlsx File\n",
    "\t- Reading .csv File - Iris Dataset\n",
    "5. Non-Visual Data Analysis using Pandas (Statistical Analysis)\n",
    "\t- sum()\n",
    "\t- min() and max()\n",
    "\t- mean(), median(), var() and std()\n",
    "\t- describe() to summarize the data\n",
    "\t- corr(), skew() and kurt()\n",
    "\t- count(), unique() and value_counts() for categorical column\n",
    "\t- DataFrame.agg()\n",
    "6. Accessing Data in a DataFrame using Indexing and Slicing in Pandas DataFrame\n",
    "\t- Reading .csv File - Weather Dataset\n",
    "\t- Filtering Single Column vs Multiple Columns from a ` DataFrame`\n",
    "\t- Filtering Rows from a `DataFrame`\n",
    "\t- Filtering specific rows and columns from a `DataFrame`\n",
    "\t- loc() vs iloc()\n",
    "7. Renaming Columns, Modifying DataTypes, Creating New Columns and Deleting Columns in Pandas DataFrame\n",
    "\t- Reading .csv File - Retail Store Sales Data\n",
    "\t- Renaming Columns\n",
    "    - Modifying Columns DataTypes\n",
    "\t- Creating a Derived Column\n",
    "\t- Creating columns using apply() function\n",
    "    - Deleting column(s) in DataFrame\n",
    "8. Adding/Inserting Row(s)\n",
    "\t- Reading .xlsx File - Weather Data\n",
    "\t- Insert Row(s) using pandas.concat()\n",
    "\t- Inserting a Row using List - .loc[] and .iloc[]\n",
    "\t- Inserting a Row at a Specific Index of a DataFrame\n",
    "\t- Saving DataFrame to .xlsx\n",
    "9. Handling TimeSeries Data\n",
    "\t- Reading .csv File - Online Store Sales Data\n",
    "\t- pd.to_datetime()\n",
    "\t- Working with DateTime in Pandas\n",
    "\t- Creating a Column containing only the Order Month\n",
    "\t- Calculating Delivery Time from Order Date and Ship Date\n",
    "\t- pandas.Timedelta\n",
    "\t- Creating a Column containing Delivery Time in Number of Days\n",
    "\t- Improve Performance by Setting Date Column as the Index\n",
    "\t- Sorting Data Based on Index vs Values and Resetting Index\n",
    "10. Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Introduction to Pandas**\n",
    "\n",
    "### **Question: What is Pandas?**  \n",
    "**Answer:** `pandas` is a Python package providing **fast, flexible, and expressive data structures** designed to make working with `relational or labeled data` both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.\n",
    "\n",
    "### **Question: What kind of data does Pandas handle?**  \n",
    "**Answer:** When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data.\n",
    "\n",
    "**Reference:** https://pandas.pydata.org/docs/getting_started/index.html\n",
    "\n",
    "### **Question: What are the Data Structures in Pandas?**  \n",
    "**Answer:**  Pandas provides two types of classes for handling data:\n",
    "1. **Series:** a one-dimensional labeled array holding data of any type such as integers, strings, Python objects etc.\n",
    "2. **DataFrame:** a two-dimensional data structure that holds data like a two-dimension array or a table with rows and columns.\n",
    "\n",
    "\n",
    "### **Question: How do I read and write tabular data?**  \n",
    "**Answer:** pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these data sources is provided by function with the prefix `read_*`. Similarly, the `to_*` methods are used to store data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Installation**\n",
    "\n",
    "**Installation Command**  \n",
    "<code>! pip install pandas</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\kanav\\anaconda3\\lib\\site-packages (1.4.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\kanav\\anaconda3\\lib\\site-packages (from pandas) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\kanav\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\users\\kanav\\anaconda3\\lib\\site-packages (from pandas) (1.21.5)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\kanav\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Importing Pandas Module**\n",
    "\n",
    "**Importing Pandas**  \n",
    "<code>import pandas as pd</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Series**\n",
    "`Series` is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the **index**.  \n",
    "\n",
    "The basic method to create a `Series` is to call:  \n",
    "<code>s = pd.Series(data, index=index)</code>  \n",
    "\n",
    "Here, data can be many different things:\n",
    "1. a Python list or dict  \n",
    "2. an ndarray  \n",
    "3. a scalar value (like 5)\n",
    "\n",
    "**Important Note:** Series data structures are `value-mutable` (the values they contain can be altered) but `not size-mutable`. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Creating Series using Python list or dict**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# pd.Series(data,index)\n",
    "# index-> Unique, Hashable, same length as data. By default np.arange(n)\n",
    "import pandas as pd\n",
    "\n",
    "s = pd.Series([1, 2, 3, 4])\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      x\n",
      "1      y\n",
      "2      z\n",
      "3    abc\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(['x', 'y', 'z', 'abc'])\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     kanav\n",
      "1    bansal\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(['kanav', 'bansal'])\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b    1\n",
      "a    0\n",
      "c    2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "d = {\"b\": 1, \"a\": 0, \"c\": 2}\n",
    "\n",
    "s = pd.Series(d)\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Creating Series from Numpy ndarray**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10\n",
      "1    20\n",
      "2    30\n",
      "3    40\n",
      "4    50\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "data = np.array([10, 20, 30, 40, 50])\n",
    "\n",
    "s = pd.Series(data)\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "\n",
    "# s = pd.Series(data)\n",
    "\n",
    "# print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Creating Series from scalar**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.0\n",
       "b    5.0\n",
       "c    5.0\n",
       "d    5.0\n",
       "e    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(5.0, index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Accessing Properties/Attributes and Methods of Series**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = np.array([10, 20, 30, 40, 50, 60, 70, 80])\n",
    "\n",
    "s = pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Type: int32\n",
      "Shape: (8,)\n",
      "Values: [10 20 30 40 50 60 70 80]\n",
      "Array: <PandasArray>\n",
      "[10, 20, 30, 40, 50, 60, 70, 80]\n",
      "Length: 8, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(\"Data Type:\", s.dtype)\n",
    "print(\"Shape:\", s.shape)\n",
    "print(\"Values:\", s.values)\n",
    "print(\"Array:\", s.array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method to extract actual numpy ndarray: [10 20 30 40 50 60 70 80]\n"
     ]
    }
   ],
   "source": [
    "print(\"Method to extract actual numpy ndarray:\", s.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "4    50\n",
       "dtype: int32"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    40\n",
       "4    50\n",
       "5    60\n",
       "6    70\n",
       "7    80\n",
       "dtype: int32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Series name: None\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "8 non-null      int32\n",
      "dtypes: int32(1)\n",
      "memory usage: 160.0 bytes\n"
     ]
    }
   ],
   "source": [
    "s.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Accessing data using Indexing and Slicing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3, 4, 5])\n",
    "\n",
    "print(s[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(s[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(s[1:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(s[[1, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(s['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(s['a':])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Retrieve multiple elements\n",
    "\n",
    "print(s[['a', 'b', 'e']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'f'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3628\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3629\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'f'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16792\\1411601391.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'f'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    956\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    957\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 958\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    959\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    960\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1067\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1068\u001b[0m         \u001b[1;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1069\u001b[1;33m         \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1070\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1071\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3629\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3631\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3632\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3633\u001b[0m                 \u001b[1;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'f'"
     ]
    }
   ],
   "source": [
    "print(s['f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "# Using the Series.get() method, a missing label will return None or specified default\n",
    "print(s.get(\"f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    }
   ],
   "source": [
    "print(s.get(\"f\", np.nan))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Pandas Data Structure - DataFrame**\n",
    "\n",
    "Pandas is a general 2D labeled, **value and size-mutable** tabular structure with potentially heterogeneously-typed column.\n",
    "\n",
    "**Important Note:** Pandas data structures are `value-mutable` (the values they contain can be altered) as well as `size-mutable`. \n",
    "\n",
    "\n",
    "<img style=\"float: right;\" width=\"300\" height=\"300\" src=\"image/01_table_dataframe.PNG\">\n",
    "\n",
    "**Question: What kind of data does pandas handle?**  \n",
    "**Answer:** When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data. In pandas, a data table is called a DataFrame.  \n",
    "\n",
    "#### Remember\n",
    "> Import the package, aka `import pandas as pd`  \n",
    "> A table of data is stored as a pandas `DataFrame`  \n",
    "> Each column in a DataFrame is a `Series`  \n",
    "> You can do things by `applying a method` to a DataFrame or Series  \n",
    "\n",
    "### **Creating a Pandas DataFrame**\n",
    "**Syntax**  \n",
    "<code>df = pd.DataFrame(data, index=idxs, columns=cols)</code>  \n",
    "\n",
    "Here data can be many different things:\n",
    "1. Python Dict, List or Tuple  \n",
    "2. Numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Creating DataFrame using Python dict, list or tuple**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T17:10:12.958091Z",
     "start_time": "2018-08-10T17:10:12.948157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jack</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ricky</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name   Age  Gender\n",
       "0    Tom  28.0    Male\n",
       "1   Jack  34.0  Female\n",
       "2  Steve   NaN  Female\n",
       "3  Ricky  42.0    Male"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dataframe using Python Dictionary\n",
    "\n",
    "data = {\n",
    "        'Name': ['Tom', 'Jack', 'Steve', 'Ricky'], \n",
    "        'Age': [28,34,np.nan,42],\n",
    "        'Gender': ['Male', 'Female', 'Female', 'Male']\n",
    "       }\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/1/2019</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/1/2019</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/1/2019</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/1/2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0   1  2      3\n",
       "0  1/1/2019  13  6   Rain\n",
       "1  2/1/2019  11  7    Fog\n",
       "2  3/1/2019  12  8  Sunny\n",
       "3  4/1/2019   8  5   Snow\n",
       "4  5/1/2019   9  6   Rain"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe using Tuple/list\n",
    "\n",
    "data = [('1/1/2019', 13, 6, 'Rain'),\n",
    "       ('2/1/2019', 11, 7, 'Fog'),\n",
    "       ('3/1/2019', 12, 8, 'Sunny'),\n",
    "       ('4/1/2019', 8, 5, 'Snow'),\n",
    "       ('5/1/2019', 9, 6, 'Rain')]\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/1/2019</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/1/2019</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/1/2019</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/1/2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Day  Temperature  WindSpeed  Event\n",
       "0  1/1/2019           13          6   Rain\n",
       "1  2/1/2019           11          7    Fog\n",
       "2  3/1/2019           12          8  Sunny\n",
       "3  4/1/2019            8          5   Snow\n",
       "4  5/1/2019            9          6   Rain"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe using Tuple/list\n",
    "\n",
    "data = (('1/1/2019', 13, 6, 'Rain'),\n",
    "       ('2/1/2019', 11, 7, 'Fog'),\n",
    "       ('3/1/2019', 12, 8, 'Sunny'),\n",
    "       ('4/1/2019', 8, 5, 'Snow'),\n",
    "       ('5/1/2019', 9, 6, 'Rain'))\n",
    "\n",
    "df = pd.DataFrame(data, columns=['Day', 'Temperature', 'WindSpeed', 'Event'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2</th>\n",
       "      <td>2/1/2019</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I3</th>\n",
       "      <td>3/1/2019</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4</th>\n",
       "      <td>4/1/2019</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I5</th>\n",
       "      <td>5/1/2019</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day  Temperature  WindSpeed  Event\n",
       "I1  1/1/2019           13          6   Rain\n",
       "I2  2/1/2019           11          7    Fog\n",
       "I3  3/1/2019           12          8  Sunny\n",
       "I4  4/1/2019            8          5   Snow\n",
       "I5  5/1/2019            9          6   Rain"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe using Tuple/list\n",
    "\n",
    "data = (['1/1/2019', 13, 6, 'Rain'],\n",
    "       ['2/1/2019', 11, 7, 'Fog'],\n",
    "       ['3/1/2019', 12, 8, 'Sunny'],\n",
    "       ['4/1/2019', 8, 5, 'Snow'],\n",
    "       ['5/1/2019', 9, 6, 'Rain'])\n",
    "\n",
    "df = pd.DataFrame(data, \n",
    "                  index=['I1', 'I2', 'I3', 'I4', 'I5'], \n",
    "                  columns=['Day', 'Temperature', 'WindSpeed', 'Event'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(type(df['Temperature']))\n",
    "\n",
    "# print(type(df[['Temperature']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Creating DataFrame using Numpy Array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 100)\n"
     ]
    }
   ],
   "source": [
    "arr = np.random.randint(100, 1999, size=(1000, 100))\n",
    "\n",
    "print(arr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>1702</td>\n",
       "      <td>1692</td>\n",
       "      <td>715</td>\n",
       "      <td>609</td>\n",
       "      <td>1947</td>\n",
       "      <td>1768</td>\n",
       "      <td>969</td>\n",
       "      <td>1659</td>\n",
       "      <td>276</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>1921</td>\n",
       "      <td>586</td>\n",
       "      <td>1219</td>\n",
       "      <td>1721</td>\n",
       "      <td>1498</td>\n",
       "      <td>739</td>\n",
       "      <td>1618</td>\n",
       "      <td>1505</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>588</td>\n",
       "      <td>436</td>\n",
       "      <td>1550</td>\n",
       "      <td>1659</td>\n",
       "      <td>1241</td>\n",
       "      <td>1215</td>\n",
       "      <td>355</td>\n",
       "      <td>176</td>\n",
       "      <td>1787</td>\n",
       "      <td>178</td>\n",
       "      <td>...</td>\n",
       "      <td>397</td>\n",
       "      <td>1751</td>\n",
       "      <td>476</td>\n",
       "      <td>361</td>\n",
       "      <td>743</td>\n",
       "      <td>1593</td>\n",
       "      <td>485</td>\n",
       "      <td>1189</td>\n",
       "      <td>413</td>\n",
       "      <td>1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>943</td>\n",
       "      <td>662</td>\n",
       "      <td>1644</td>\n",
       "      <td>1087</td>\n",
       "      <td>1166</td>\n",
       "      <td>634</td>\n",
       "      <td>1359</td>\n",
       "      <td>928</td>\n",
       "      <td>1530</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>1628</td>\n",
       "      <td>397</td>\n",
       "      <td>776</td>\n",
       "      <td>1241</td>\n",
       "      <td>305</td>\n",
       "      <td>731</td>\n",
       "      <td>1079</td>\n",
       "      <td>1329</td>\n",
       "      <td>869</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>380</td>\n",
       "      <td>1799</td>\n",
       "      <td>946</td>\n",
       "      <td>1321</td>\n",
       "      <td>204</td>\n",
       "      <td>1881</td>\n",
       "      <td>136</td>\n",
       "      <td>842</td>\n",
       "      <td>1221</td>\n",
       "      <td>254</td>\n",
       "      <td>...</td>\n",
       "      <td>1143</td>\n",
       "      <td>1402</td>\n",
       "      <td>1882</td>\n",
       "      <td>1733</td>\n",
       "      <td>1525</td>\n",
       "      <td>902</td>\n",
       "      <td>905</td>\n",
       "      <td>1889</td>\n",
       "      <td>770</td>\n",
       "      <td>1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1888</td>\n",
       "      <td>1021</td>\n",
       "      <td>417</td>\n",
       "      <td>537</td>\n",
       "      <td>1658</td>\n",
       "      <td>1802</td>\n",
       "      <td>1687</td>\n",
       "      <td>550</td>\n",
       "      <td>527</td>\n",
       "      <td>954</td>\n",
       "      <td>...</td>\n",
       "      <td>727</td>\n",
       "      <td>1370</td>\n",
       "      <td>433</td>\n",
       "      <td>756</td>\n",
       "      <td>1846</td>\n",
       "      <td>1102</td>\n",
       "      <td>726</td>\n",
       "      <td>685</td>\n",
       "      <td>1559</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>826</td>\n",
       "      <td>1375</td>\n",
       "      <td>218</td>\n",
       "      <td>1914</td>\n",
       "      <td>1159</td>\n",
       "      <td>1181</td>\n",
       "      <td>1403</td>\n",
       "      <td>769</td>\n",
       "      <td>1670</td>\n",
       "      <td>399</td>\n",
       "      <td>...</td>\n",
       "      <td>1051</td>\n",
       "      <td>1707</td>\n",
       "      <td>1855</td>\n",
       "      <td>160</td>\n",
       "      <td>106</td>\n",
       "      <td>1060</td>\n",
       "      <td>1695</td>\n",
       "      <td>1975</td>\n",
       "      <td>732</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1480</td>\n",
       "      <td>957</td>\n",
       "      <td>659</td>\n",
       "      <td>371</td>\n",
       "      <td>638</td>\n",
       "      <td>1805</td>\n",
       "      <td>1642</td>\n",
       "      <td>531</td>\n",
       "      <td>980</td>\n",
       "      <td>1992</td>\n",
       "      <td>...</td>\n",
       "      <td>1432</td>\n",
       "      <td>1914</td>\n",
       "      <td>1057</td>\n",
       "      <td>991</td>\n",
       "      <td>985</td>\n",
       "      <td>398</td>\n",
       "      <td>224</td>\n",
       "      <td>369</td>\n",
       "      <td>1256</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>515</td>\n",
       "      <td>351</td>\n",
       "      <td>1377</td>\n",
       "      <td>1116</td>\n",
       "      <td>693</td>\n",
       "      <td>1478</td>\n",
       "      <td>1737</td>\n",
       "      <td>1803</td>\n",
       "      <td>1133</td>\n",
       "      <td>1972</td>\n",
       "      <td>...</td>\n",
       "      <td>913</td>\n",
       "      <td>1243</td>\n",
       "      <td>1942</td>\n",
       "      <td>1807</td>\n",
       "      <td>1632</td>\n",
       "      <td>1886</td>\n",
       "      <td>792</td>\n",
       "      <td>655</td>\n",
       "      <td>219</td>\n",
       "      <td>1167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>212</td>\n",
       "      <td>177</td>\n",
       "      <td>281</td>\n",
       "      <td>1247</td>\n",
       "      <td>1161</td>\n",
       "      <td>821</td>\n",
       "      <td>1535</td>\n",
       "      <td>1217</td>\n",
       "      <td>1844</td>\n",
       "      <td>1659</td>\n",
       "      <td>...</td>\n",
       "      <td>115</td>\n",
       "      <td>788</td>\n",
       "      <td>1899</td>\n",
       "      <td>1937</td>\n",
       "      <td>893</td>\n",
       "      <td>198</td>\n",
       "      <td>262</td>\n",
       "      <td>797</td>\n",
       "      <td>1942</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>418</td>\n",
       "      <td>1402</td>\n",
       "      <td>1435</td>\n",
       "      <td>391</td>\n",
       "      <td>1778</td>\n",
       "      <td>1733</td>\n",
       "      <td>644</td>\n",
       "      <td>1822</td>\n",
       "      <td>1884</td>\n",
       "      <td>437</td>\n",
       "      <td>...</td>\n",
       "      <td>495</td>\n",
       "      <td>1765</td>\n",
       "      <td>686</td>\n",
       "      <td>457</td>\n",
       "      <td>400</td>\n",
       "      <td>652</td>\n",
       "      <td>1021</td>\n",
       "      <td>766</td>\n",
       "      <td>1517</td>\n",
       "      <td>1389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2     3     4     5     6     7     8     9   ...    90  \\\n",
       "0     100  1702  1692   715   609  1947  1768   969  1659   276  ...   132   \n",
       "1     588   436  1550  1659  1241  1215   355   176  1787   178  ...   397   \n",
       "2     943   662  1644  1087  1166   634  1359   928  1530   149  ...  1628   \n",
       "3     380  1799   946  1321   204  1881   136   842  1221   254  ...  1143   \n",
       "4    1888  1021   417   537  1658  1802  1687   550   527   954  ...   727   \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "995   826  1375   218  1914  1159  1181  1403   769  1670   399  ...  1051   \n",
       "996  1480   957   659   371   638  1805  1642   531   980  1992  ...  1432   \n",
       "997   515   351  1377  1116   693  1478  1737  1803  1133  1972  ...   913   \n",
       "998   212   177   281  1247  1161   821  1535  1217  1844  1659  ...   115   \n",
       "999   418  1402  1435   391  1778  1733   644  1822  1884   437  ...   495   \n",
       "\n",
       "       91    92    93    94    95    96    97    98    99  \n",
       "0    1921   586  1219  1721  1498   739  1618  1505   258  \n",
       "1    1751   476   361   743  1593   485  1189   413  1375  \n",
       "2     397   776  1241   305   731  1079  1329   869  1949  \n",
       "3    1402  1882  1733  1525   902   905  1889   770  1576  \n",
       "4    1370   433   756  1846  1102   726   685  1559   632  \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "995  1707  1855   160   106  1060  1695  1975   732  1927  \n",
       "996  1914  1057   991   985   398   224   369  1256   593  \n",
       "997  1243  1942  1807  1632  1886   792   655   219  1167  \n",
       "998   788  1899  1937   893   198   262   797  1942   194  \n",
       "999  1765   686   457   400   652  1021   766  1517  1389  \n",
       "\n",
       "[1000 rows x 100 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arr)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>588</td>\n",
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       "      <td>1375</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>397</td>\n",
       "      <td>776</td>\n",
       "      <td>1241</td>\n",
       "      <td>305</td>\n",
       "      <td>731</td>\n",
       "      <td>1079</td>\n",
       "      <td>1329</td>\n",
       "      <td>869</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>380</td>\n",
       "      <td>1799</td>\n",
       "      <td>946</td>\n",
       "      <td>1321</td>\n",
       "      <td>204</td>\n",
       "      <td>1881</td>\n",
       "      <td>136</td>\n",
       "      <td>842</td>\n",
       "      <td>1221</td>\n",
       "      <td>254</td>\n",
       "      <td>...</td>\n",
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       "      <td>1402</td>\n",
       "      <td>1882</td>\n",
       "      <td>1733</td>\n",
       "      <td>1525</td>\n",
       "      <td>902</td>\n",
       "      <td>905</td>\n",
       "      <td>1889</td>\n",
       "      <td>770</td>\n",
       "      <td>1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1888</td>\n",
       "      <td>1021</td>\n",
       "      <td>417</td>\n",
       "      <td>537</td>\n",
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       "      <td>1802</td>\n",
       "      <td>1687</td>\n",
       "      <td>550</td>\n",
       "      <td>527</td>\n",
       "      <td>954</td>\n",
       "      <td>...</td>\n",
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       "      <td>1370</td>\n",
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       "      <td>756</td>\n",
       "      <td>1846</td>\n",
       "      <td>1102</td>\n",
       "      <td>726</td>\n",
       "      <td>685</td>\n",
       "      <td>1559</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>826</td>\n",
       "      <td>1375</td>\n",
       "      <td>218</td>\n",
       "      <td>1914</td>\n",
       "      <td>1159</td>\n",
       "      <td>1181</td>\n",
       "      <td>1403</td>\n",
       "      <td>769</td>\n",
       "      <td>1670</td>\n",
       "      <td>399</td>\n",
       "      <td>...</td>\n",
       "      <td>1051</td>\n",
       "      <td>1707</td>\n",
       "      <td>1855</td>\n",
       "      <td>160</td>\n",
       "      <td>106</td>\n",
       "      <td>1060</td>\n",
       "      <td>1695</td>\n",
       "      <td>1975</td>\n",
       "      <td>732</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1480</td>\n",
       "      <td>957</td>\n",
       "      <td>659</td>\n",
       "      <td>371</td>\n",
       "      <td>638</td>\n",
       "      <td>1805</td>\n",
       "      <td>1642</td>\n",
       "      <td>531</td>\n",
       "      <td>980</td>\n",
       "      <td>1992</td>\n",
       "      <td>...</td>\n",
       "      <td>1432</td>\n",
       "      <td>1914</td>\n",
       "      <td>1057</td>\n",
       "      <td>991</td>\n",
       "      <td>985</td>\n",
       "      <td>398</td>\n",
       "      <td>224</td>\n",
       "      <td>369</td>\n",
       "      <td>1256</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>515</td>\n",
       "      <td>351</td>\n",
       "      <td>1377</td>\n",
       "      <td>1116</td>\n",
       "      <td>693</td>\n",
       "      <td>1478</td>\n",
       "      <td>1737</td>\n",
       "      <td>1803</td>\n",
       "      <td>1133</td>\n",
       "      <td>1972</td>\n",
       "      <td>...</td>\n",
       "      <td>913</td>\n",
       "      <td>1243</td>\n",
       "      <td>1942</td>\n",
       "      <td>1807</td>\n",
       "      <td>1632</td>\n",
       "      <td>1886</td>\n",
       "      <td>792</td>\n",
       "      <td>655</td>\n",
       "      <td>219</td>\n",
       "      <td>1167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>212</td>\n",
       "      <td>177</td>\n",
       "      <td>281</td>\n",
       "      <td>1247</td>\n",
       "      <td>1161</td>\n",
       "      <td>821</td>\n",
       "      <td>1535</td>\n",
       "      <td>1217</td>\n",
       "      <td>1844</td>\n",
       "      <td>1659</td>\n",
       "      <td>...</td>\n",
       "      <td>115</td>\n",
       "      <td>788</td>\n",
       "      <td>1899</td>\n",
       "      <td>1937</td>\n",
       "      <td>893</td>\n",
       "      <td>198</td>\n",
       "      <td>262</td>\n",
       "      <td>797</td>\n",
       "      <td>1942</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>418</td>\n",
       "      <td>1402</td>\n",
       "      <td>1435</td>\n",
       "      <td>391</td>\n",
       "      <td>1778</td>\n",
       "      <td>1733</td>\n",
       "      <td>644</td>\n",
       "      <td>1822</td>\n",
       "      <td>1884</td>\n",
       "      <td>437</td>\n",
       "      <td>...</td>\n",
       "      <td>495</td>\n",
       "      <td>1765</td>\n",
       "      <td>686</td>\n",
       "      <td>457</td>\n",
       "      <td>400</td>\n",
       "      <td>652</td>\n",
       "      <td>1021</td>\n",
       "      <td>766</td>\n",
       "      <td>1517</td>\n",
       "      <td>1389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     col_1  col_2  col_3  col_4  col_5  col_6  col_7  col_8  col_9  col_10  \\\n",
       "0      100   1702   1692    715    609   1947   1768    969   1659     276   \n",
       "1      588    436   1550   1659   1241   1215    355    176   1787     178   \n",
       "2      943    662   1644   1087   1166    634   1359    928   1530     149   \n",
       "3      380   1799    946   1321    204   1881    136    842   1221     254   \n",
       "4     1888   1021    417    537   1658   1802   1687    550    527     954   \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "995    826   1375    218   1914   1159   1181   1403    769   1670     399   \n",
       "996   1480    957    659    371    638   1805   1642    531    980    1992   \n",
       "997    515    351   1377   1116    693   1478   1737   1803   1133    1972   \n",
       "998    212    177    281   1247   1161    821   1535   1217   1844    1659   \n",
       "999    418   1402   1435    391   1778   1733    644   1822   1884     437   \n",
       "\n",
       "     ...  col_91  col_92  col_93  col_94  col_95  col_96  col_97  col_98  \\\n",
       "0    ...     132    1921     586    1219    1721    1498     739    1618   \n",
       "1    ...     397    1751     476     361     743    1593     485    1189   \n",
       "2    ...    1628     397     776    1241     305     731    1079    1329   \n",
       "3    ...    1143    1402    1882    1733    1525     902     905    1889   \n",
       "4    ...     727    1370     433     756    1846    1102     726     685   \n",
       "..   ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "995  ...    1051    1707    1855     160     106    1060    1695    1975   \n",
       "996  ...    1432    1914    1057     991     985     398     224     369   \n",
       "997  ...     913    1243    1942    1807    1632    1886     792     655   \n",
       "998  ...     115     788    1899    1937     893     198     262     797   \n",
       "999  ...     495    1765     686     457     400     652    1021     766   \n",
       "\n",
       "     col_99  col_100  \n",
       "0      1505      258  \n",
       "1       413     1375  \n",
       "2       869     1949  \n",
       "3       770     1576  \n",
       "4      1559      632  \n",
       "..      ...      ...  \n",
       "995     732     1927  \n",
       "996    1256      593  \n",
       "997     219     1167  \n",
       "998    1942      194  \n",
       "999    1517     1389  \n",
       "\n",
       "[1000 rows x 100 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(arr, columns=[\"col_\"+str(i) for i in range(1, 101) ])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Accessing Attributes/Properties and Methods of DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T17:07:49.162583Z",
     "start_time": "2018-08-10T17:07:49.145635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>25</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jack</td>\n",
       "      <td>26</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve</td>\n",
       "      <td>25</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ricky</td>\n",
       "      <td>35</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vin</td>\n",
       "      <td>23</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>James</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vin</td>\n",
       "      <td>31</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Rating\n",
       "0    Tom   25    4.23\n",
       "1   Jack   26    4.10\n",
       "2  Steve   25    3.40\n",
       "3  Ricky   35    5.00\n",
       "4    Vin   23    2.90\n",
       "5  James   33     NaN\n",
       "6    Vin   31    3.10"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Dictionary of Series\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = {'Name':pd.Series(['Tom', 'Jack', 'Steve', 'Ricky', 'Vin', 'James', 'Vin']),\n",
    "       'Age':pd.Series([25,26,25,35,23,33,31]),\n",
    "       'Rating':pd.Series([4.23,4.1,3.4,5,2.9,np.nan,3.1])}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of DataFrame: (7, 3)\n",
      "\n",
      "Name of each column: Index(['Name', 'Age', 'Rating'], dtype='object')\n",
      "\n",
      "Data Types of each Columns:\n",
      " Name       object\n",
      "Age         int64\n",
      "Rating    float64\n",
      "dtype: object\n",
      "\n",
      "Axes:\n",
      " [RangeIndex(start=0, stop=7, step=1), Index(['Name', 'Age', 'Rating'], dtype='object')]\n",
      "\n",
      "Return data as numpy array:\n",
      " [['Tom' 25 4.23]\n",
      " ['Jack' 26 4.1]\n",
      " ['Steve' 25 3.4]\n",
      " ['Ricky' 35 5.0]\n",
      " ['Vin' 23 2.9]\n",
      " ['James' 33 nan]\n",
      " ['Vin' 31 3.1]]\n"
     ]
    }
   ],
   "source": [
    "print('Shape of DataFrame:', df.shape)\n",
    "print()\n",
    "print('Name of each column:', df.columns)\n",
    "print()\n",
    "print('Data Types of each Columns:\\n', df.dtypes)\n",
    "print()\n",
    "print('Axes:\\n', df.axes)\n",
    "print()\n",
    "print('Return data as numpy array:\\n', df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7 entries, 0 to 6\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Name    7 non-null      object \n",
      " 1   Age     7 non-null      int64  \n",
      " 2   Rating  6 non-null      float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 296.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Data types of each column\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `info()` provides technical information about a DataFrame, so let’s explain the output in more detail:\n",
    "\n",
    "> - It is indeed a `DataFrame`.  \n",
    "> - There are `7 entries`, i.e. 7 rows.  \n",
    "> - Each row has a `row label` (aka the `index`) with values ranging from `0 to 6`.  \n",
    "> - The table has `3 columns`. Name and Age columns have a value for each of the rows (all 7 values are non-null). Rating column do have missing values and less than 7 non-null values.  \n",
    "> - The column Name consists of textual data (strings, aka object). The other columns are numerical data with some of them whole numbers (aka integer) and others are real numbers (aka float).  \n",
    "> - The kind of data (characters, integers,…) in the different columns are summarized by listing the `dtypes`.  \n",
    "> - The approximate amount of RAM used to hold the DataFrame is provided as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:25:45.652390Z",
     "start_time": "2018-08-10T16:25:45.634320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>25</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jack</td>\n",
       "      <td>26</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve</td>\n",
       "      <td>25</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ricky</td>\n",
       "      <td>35</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vin</td>\n",
       "      <td>23</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Rating\n",
       "0    Tom   25    4.23\n",
       "1   Jack   26    4.10\n",
       "2  Steve   25    3.40\n",
       "3  Ricky   35    5.00\n",
       "4    Vin   23    2.90"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head -> by default head returns first 5 rows\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>25</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jack</td>\n",
       "      <td>26</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age  Rating\n",
       "0   Tom   25    4.23\n",
       "1  Jack   26    4.10"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:26:35.406634Z",
     "start_time": "2018-08-10T16:26:35.391041Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve</td>\n",
       "      <td>25</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ricky</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vin</td>\n",
       "      <td>23</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>James</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vin</td>\n",
       "      <td>31</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Rating\n",
       "2  Steve   25     3.4\n",
       "3  Ricky   35     5.0\n",
       "4    Vin   23     2.9\n",
       "5  James   33     NaN\n",
       "6    Vin   31     3.1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail -> by default tail returns last 5 rows\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>James</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vin</td>\n",
       "      <td>31</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Rating\n",
       "5  James   33     NaN\n",
       "6    Vin   31     3.1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Working with Tabular Data**\n",
    "\n",
    "**Question: How do I read and write tabular data?**  \n",
    "**Answer:** pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these data sources is provided by function with the prefix `read_*`. Similarly, the `to_*` methods are used to store data.\n",
    "\n",
    "#### Remember\n",
    "> Getting data in to pandas from many different file formats or data sources is supported by `read_*` functions.  \n",
    "> Exporting data out of pandas is provided by different `to_*` methods.  \n",
    "> The `head/tail/info` methods and the `dtypes` attribute are convenient for a first check.  \n",
    "\n",
    "<img width=\"600\" height=\"600\" src=\"image/02_io_readwrite.PNG\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Dataframe to .csv & .xlsx**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Create Dictionary of Series\n",
    "data = {'Name':pd.Series(['Tom', 'Jack', 'Steve', 'Ricky', 'Vin', 'James', 'Smith']),\n",
    "       'Age':pd.Series([25,26,25,35,23,33,31]),\n",
    "       'Rating':pd.Series([4.23,4.1,3.4,5,np.nan,4.7,3.1])}\n",
    "\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>25</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jack</td>\n",
       "      <td>26</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve</td>\n",
       "      <td>25</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ricky</td>\n",
       "      <td>35</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vin</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Rating\n",
       "0    Tom   25    4.23\n",
       "1   Jack   26    4.10\n",
       "2  Steve   25    3.40\n",
       "3  Ricky   35    5.00\n",
       "4    Vin   23     NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve</td>\n",
       "      <td>25</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ricky</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vin</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>James</td>\n",
       "      <td>33</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smith</td>\n",
       "      <td>31</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Rating\n",
       "2  Steve   25     3.4\n",
       "3  Ricky   35     5.0\n",
       "4    Vin   23     NaN\n",
       "5  James   33     4.7\n",
       "6  Smith   31     3.1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7 entries, 0 to 6\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Name    7 non-null      object \n",
      " 1   Age     7 non-null      int64  \n",
      " 2   Rating  6 non-null      float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 296.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Dataframe to CSV\n",
    "\n",
    "df.to_csv('data/temp/new_csv_file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Dataframe to CSV without index\n",
    "\n",
    "df.to_csv('data/temp/new_csv_file_no_index.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Dataframe to XLSX\n",
    "\n",
    "df.to_excel('data/temp/new_excel_file.xlsx', sheet_name='stud_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Dataframe to XLSX without index\n",
    "\n",
    "df.to_excel('data/temp/new_excel_file_noIndex.xlsx', sheet_name='stud_data', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Reading .xlsx File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel('data/weather_data.xlsx')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6 entries, 0 to 5\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   day          6 non-null      object\n",
      " 1   temperature  6 non-null      int64 \n",
      " 2   windspeed    6 non-null      int64 \n",
      " 3   event        6 non-null      object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Reading .csv File - Iris Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-07T06:05:59.875584Z",
     "start_time": "2018-06-07T06:05:59.782833Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/Iris.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Description**  \n",
    "The Iris Dataset contains four features (length and width of sepals and petals) of 50 samples of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). \n",
    "\n",
    "The iris data set is widely used as a beginner's dataset for machine learning purposes.  \n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td> \n",
    "            <p align=\"center\">\n",
    "                <img src=\"image/04_iris_setosa.jpg\" width=\"150\" /> \n",
    "                <br>\n",
    "                <em style=\"color: grey\">Iris Setosa</em> \n",
    "            </p>             \n",
    "        </td>\n",
    "        <td> \n",
    "            <p align=\"center\">\n",
    "                <img src=\"image/05_iris_versicolor.jpg\" width=\"250\" /> \n",
    "                <br>\n",
    "                <em style=\"color: grey\">Iris Versicolor</em> \n",
    "            </p>\n",
    "        </td>\n",
    "        <td> \n",
    "            <p align=\"center\">\n",
    "                <img src=\"image/06_iris_virginica.jpg\" width=\"250\" /> \n",
    "                <br>\n",
    "                <em style=\"color: grey\">Iris Virginica</em> \n",
    "            </p>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Dataframe to CSV\n",
    "\n",
    "df.to_csv('data/temp/new_iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write Dataframe to CSV\n",
    "\n",
    "df.to_csv('data/temp/new_iris_no_index.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Non-Visual Data Analysis using Pandas (Statistical Analysis)**\n",
    "\n",
    "<img style=\"float: right;\" width=\"300\" height=\"300\" src=\"image/03_reduction.PNG\">\n",
    "\n",
    "**Question: How to calculate summary statistics?**  \n",
    "**Answer:** Basic statistics (mean, median, min, max, counts…) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine approach.\n",
    "\n",
    "#### Remember\n",
    "> Aggregation statistics(mean, median, min, max, counts…) can be calculated on entire columns or rows.  \n",
    "> `groupby` provides the power of the split-apply-combine pattern.  \n",
    "> `value_counts` is a convenient shortcut to count the number of entries in each category of a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-10T16:28:57.418141Z",
     "start_time": "2018-08-10T16:28:57.409164Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/Iris.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-07T06:06:14.307489Z",
     "start_time": "2018-06-07T06:06:14.293530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-07T06:09:00.507842Z",
     "start_time": "2018-06-07T06:09:00.502868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-07T06:09:22.661615Z",
     "start_time": "2018-06-07T06:09:22.656629Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:08:27.529377Z",
     "start_time": "2018-06-06T16:08:27.522759Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                                                           11325\n",
       "SepalLengthCm                                                876.5\n",
       "SepalWidthCm                                                 458.1\n",
       "PetalLengthCm                                                563.8\n",
       "PetalWidthCm                                                 179.8\n",
       "Species          Iris-setosaIris-setosaIris-setosaIris-setosaIr...\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum()-> returns the sum of values for requested axis. by default axis = 0\n",
    "\n",
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:18:00.311731Z",
     "start_time": "2018-06-06T16:18:00.305934Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kanav\\AppData\\Local\\Temp\\ipykernel_16792\\1542405310.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.sum(axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       11.2\n",
       "1       11.5\n",
       "2       12.4\n",
       "3       13.4\n",
       "4       15.2\n",
       "       ...  \n",
       "145    163.2\n",
       "146    162.7\n",
       "147    164.7\n",
       "148    166.3\n",
       "149    165.8\n",
       "Length: 150, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis = 1 -> row wise sum\n",
    "\n",
    "df.sum(axis=1)\n",
    "\n",
    "# How to fix the warning ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      10.2\n",
       "1       9.5\n",
       "2       9.4\n",
       "3       9.4\n",
       "4      10.2\n",
       "       ... \n",
       "145    17.2\n",
       "146    15.7\n",
       "147    16.7\n",
       "148    17.3\n",
       "149    15.8\n",
       "Length: 150, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### min() and max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         1\n",
       "SepalLengthCm            4.3\n",
       "SepalWidthCm             2.0\n",
       "PetalLengthCm            1.0\n",
       "PetalWidthCm             0.1\n",
       "Species          Iris-setosa\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                          150\n",
       "SepalLengthCm               7.9\n",
       "SepalWidthCm                4.4\n",
       "PetalLengthCm               6.9\n",
       "PetalWidthCm                2.5\n",
       "Species          Iris-virginica\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mean(), median(), var() and std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:20:07.047891Z",
     "start_time": "2018-06-06T16:20:07.033137Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kanav\\AppData\\Local\\Temp\\ipykernel_16792\\2950943871.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id               75.500000\n",
       "SepalLengthCm     5.843333\n",
       "SepalWidthCm      3.054000\n",
       "PetalLengthCm     3.758667\n",
       "PetalWidthCm      1.198667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean()\n",
    "\n",
    "df.mean()\n",
    "\n",
    "# How to fix the warning ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    5.843333\n",
       "SepalWidthCm     3.054000\n",
       "PetalLengthCm    3.758667\n",
       "PetalWidthCm     1.198667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "SepalLengthCm    float64\n",
       "SepalWidthCm     float64\n",
       "PetalLengthCm    float64\n",
       "PetalWidthCm     float64\n",
       "Species           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Syntax: DataFrame.select_dtypes(include=None, exclude=None)\n",
    "num_cols = df.select_dtypes(include=['float64']).columns\n",
    "\n",
    "print(num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    5.80\n",
       "SepalWidthCm     3.00\n",
       "PetalLengthCm    4.35\n",
       "PetalWidthCm     1.30\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0.685694\n",
       "SepalWidthCm     0.188004\n",
       "PetalLengthCm    3.113179\n",
       "PetalWidthCm     0.582414\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:20:27.196693Z",
     "start_time": "2018-06-06T16:20:27.185632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0.828066\n",
       "SepalWidthCm     0.433594\n",
       "PetalLengthCm    1.764420\n",
       "PetalWidthCm     0.763161\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# std()\n",
    "df[num_cols].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### count(), nunique(), unique() and value_counts() for categorical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### describe() to summarize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:21:03.420633Z",
     "start_time": "2018-06-06T16:21:03.389280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() -> summarizing the data\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:23:20.505379Z",
     "start_time": "2018-06-06T16:23:20.484877Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Species\n",
       "count           150\n",
       "unique            3\n",
       "top     Iris-setosa\n",
       "freq             50"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# include object, number, all\n",
    "\n",
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:24:38.704357Z",
     "start_time": "2018-06-06T16:24:38.684945Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-06T16:24:23.622839Z",
     "start_time": "2018-06-06T16:24:23.597741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "count   150.000000     150.000000    150.000000     150.000000    150.000000   \n",
       "unique         NaN            NaN           NaN            NaN           NaN   \n",
       "top            NaN            NaN           NaN            NaN           NaN   \n",
       "freq           NaN            NaN           NaN            NaN           NaN   \n",
       "mean     75.500000       5.843333      3.054000       3.758667      1.198667   \n",
       "std      43.445368       0.828066      0.433594       1.764420      0.763161   \n",
       "min       1.000000       4.300000      2.000000       1.000000      0.100000   \n",
       "25%      38.250000       5.100000      2.800000       1.600000      0.300000   \n",
       "50%      75.500000       5.800000      3.000000       4.350000      1.300000   \n",
       "75%     112.750000       6.400000      3.300000       5.100000      1.800000   \n",
       "max     150.000000       7.900000      4.400000       6.900000      2.500000   \n",
       "\n",
       "            Species  \n",
       "count           150  \n",
       "unique            3  \n",
       "top     Iris-setosa  \n",
       "freq             50  \n",
       "mean            NaN  \n",
       "std             NaN  \n",
       "min             NaN  \n",
       "25%             NaN  \n",
       "50%             NaN  \n",
       "75%             NaN  \n",
       "max             NaN  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Don't pass 'all' as a list\n",
    "\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### corr(), skew() and kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716676</td>\n",
       "      <td>-0.397729</td>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.899759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>0.716676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.397729</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.899759</td>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "Id             1.000000       0.716676     -0.397729       0.882747   \n",
       "SepalLengthCm  0.716676       1.000000     -0.109369       0.871754   \n",
       "SepalWidthCm  -0.397729      -0.109369      1.000000      -0.420516   \n",
       "PetalLengthCm  0.882747       0.871754     -0.420516       1.000000   \n",
       "PetalWidthCm   0.899759       0.817954     -0.356544       0.962757   \n",
       "\n",
       "               PetalWidthCm  \n",
       "Id                 0.899759  \n",
       "SepalLengthCm      0.817954  \n",
       "SepalWidthCm      -0.356544  \n",
       "PetalLengthCm      0.962757  \n",
       "PetalWidthCm       1.000000  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kanav\\AppData\\Local\\Temp\\ipykernel_16792\\1906246515.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.skew()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id               0.000000\n",
       "SepalLengthCm    0.314911\n",
       "SepalWidthCm     0.334053\n",
       "PetalLengthCm   -0.274464\n",
       "PetalWidthCm    -0.104997\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()\n",
    "\n",
    "# How to fix this warning?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kanav\\AppData\\Local\\Temp\\ipykernel_16792\\4028397799.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.kurt()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id              -1.200000\n",
       "SepalLengthCm   -0.552064\n",
       "SepalWidthCm     0.290781\n",
       "PetalLengthCm   -1.401921\n",
       "PetalWidthCm    -1.339754\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.kurt()\n",
    "\n",
    "# How to fix this warning?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm   -0.552064\n",
       "SepalWidthCm     0.290781\n",
       "PetalLengthCm   -1.401921\n",
       "PetalWidthCm    -1.339754\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = df.select_dtypes(include=['float64']).columns\n",
    "\n",
    "df[num_cols].kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame.agg()\n",
    "Instead of the predefined statistics, specific combinations of aggregating statistics for given columns can be defined using the `DataFrame.agg()` method.  \n",
    "\n",
    "List of all the aggregating statistics can be found on below reference:  \n",
    "Reference: https://pandas.pydata.org/docs/user_guide/basics.html#basics-stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.3</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.9</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.0</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SepalLengthCm  PetalWidthCm  Species\n",
       "min               4.3      0.100000      NaN\n",
       "max               7.9      2.500000      NaN\n",
       "median            5.8           NaN      NaN\n",
       "count           150.0    150.000000    150.0\n",
       "mean              NaN      1.198667      NaN"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg(\n",
    "    {\n",
    "        \"SepalLengthCm\" : [\"min\", \"max\", \"median\", \"count\"],\n",
    "        \"PetalWidthCm\" : [\"min\", \"max\", \"mean\", \"count\"],\n",
    "        \"Species\" : [\"count\"]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Data in a DataFrame using Indexing and Slicing in `Pandas DataFrame`\n",
    "\n",
    "<img style=\"float: right;\" width=\"300\" height=\"300\" src=\"image/07_subset_columns.PNG\">\n",
    "\n",
    "**Question: How do I select a subset of a table?**  \n",
    "**Answer:** Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in pandas.\n",
    "\n",
    "\n",
    "#### Remember\n",
    "> When selecting subsets of data, square brackets [] are used.  \n",
    "> Inside these brackets, you can use a single column/row label, a list of column/row labels, a slice of labels, a conditional expression or a colon.  \n",
    "> Select specific rows and/or columns using loc when using the row and column names.  \n",
    "> Select specific rows and/or columns using iloc when using the positions in the table.  \n",
    "> You can assign new values to a selection based on loc/iloc.\n",
    "\n",
    "\n",
    "\n",
    "### Reading .csv File - Weather Dataset\n",
    "**Data Description**  \n",
    "Weather data collected from the National Weather Service. It contains the first six months of 2016, for a weather station in central park. It contains for each day the minimum temperature, maximum temperature, average temperature, precipitation, new snow fall, and current snow depth. The temperature is measured in Fahrenheit and the depth is measured in inches. T means that there is a trace of precipitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-1-2016</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-1-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3-1-2016</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4-1-2016</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-1-2016</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  maximum temperature  minimum temperature  average temperature  \\\n",
       "0  1-1-2016                   42                   34                 38.0   \n",
       "1  2-1-2016                   40                   32                 36.0   \n",
       "2  3-1-2016                   45                   35                 40.0   \n",
       "3  4-1-2016                   36                   14                 25.0   \n",
       "4  5-1-2016                   29                   11                 20.0   \n",
       "\n",
       "  precipitation snow fall snow depth  \n",
       "0          0.00       0.0          0  \n",
       "1          0.00       0.0          0  \n",
       "2          0.00       0.0          0  \n",
       "3          0.00       0.0          0  \n",
       "4          0.00       0.0          0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/nyc_weather.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of DataFrame: (366, 7)\n",
      "Features/Columns: Index(['date', 'maximum temperature', 'minimum temperature',\n",
      "       'average temperature', 'precipitation', 'snow fall', 'snow depth'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of DataFrame:\", df.shape)\n",
    "print(\"Features/Columns:\", df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.625683</td>\n",
       "      <td>49.806011</td>\n",
       "      <td>57.215847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.041787</td>\n",
       "      <td>16.570747</td>\n",
       "      <td>17.124760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>37.250000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>55.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>73.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>88.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       maximum temperature  minimum temperature  average temperature\n",
       "count           366.000000           366.000000           366.000000\n",
       "mean             64.625683            49.806011            57.215847\n",
       "std              18.041787            16.570747            17.124760\n",
       "min              15.000000            -1.000000             7.000000\n",
       "25%              50.000000            37.250000            44.000000\n",
       "50%              64.500000            48.000000            55.750000\n",
       "75%              81.000000            65.000000            73.500000\n",
       "max              96.000000            81.000000            88.500000"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "\n",
    "# Why didn't it generate precipitation, snow fall and snow depth statistical description ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 366 entries, 0 to 365\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   date                 366 non-null    object \n",
      " 1   maximum temperature  366 non-null    int64  \n",
      " 2   minimum temperature  366 non-null    int64  \n",
      " 3   average temperature  366 non-null    float64\n",
      " 4   precipitation        366 non-null    object \n",
      " 5   snow fall            366 non-null    object \n",
      " 6   snow depth           366 non-null    object \n",
      "dtypes: float64(1), int64(2), object(4)\n",
      "memory usage: 20.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.5"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the maximum of avg temperature?\n",
    "\n",
    "df['average temperature'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.80601092896175"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average of Minimum Temperature\n",
    "\n",
    "df['minimum temperature'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering Single Column vs Multiple Columns from a ` DataFrame`\n",
    "To select a single column, use square brackets [] with the column name of the column of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42\n",
       "1    40\n",
       "2    45\n",
       "3    36\n",
       "4    29\n",
       "Name: maximum temperature, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting Single Column\n",
    "\n",
    "max_temp_df = df['maximum temperature']\n",
    "\n",
    "max_temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of df['maximum temperature']: <class 'pandas.core.series.Series'>\n",
      "Shape: (366,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of df['maximum temperature']:\", type(max_temp_df))\n",
    "print(\"Shape:\", max_temp_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maximum temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   maximum temperature\n",
       "0                   42\n",
       "1                   40\n",
       "2                   45\n",
       "3                   36\n",
       "4                   29"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting Single Column\n",
    "\n",
    "max_temp_df = df[['maximum temperature']]\n",
    "\n",
    "max_temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of df['maximum temperature']: <class 'pandas.core.frame.DataFrame'>\n",
      "Shape: (366, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of df['maximum temperature']:\", type(max_temp_df))\n",
    "print(\"Shape:\", max_temp_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   maximum temperature  minimum temperature\n",
       "0                   42                   34\n",
       "1                   40                   32\n",
       "2                   45                   35\n",
       "3                   36                   14\n",
       "4                   29                   11"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting Multiple Columns\n",
    "\n",
    "temp_df = df[['maximum temperature', 'minimum temperature']]\n",
    "\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of df[['maximum temperature', 'minimum temperature']]: <class 'pandas.core.frame.DataFrame'>\n",
      "Shape: (366, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of df[['maximum temperature', 'minimum temperature']]:\", type(temp_df))\n",
    "print(\"Shape:\", temp_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering Rows from a `DataFrame`\n",
    "**Way 1**  \n",
    "We can select the rows by using slicing operation.  \n",
    "**Syntax** <code>df[ starting_row_index : ending_row_index : step ]</code>  \n",
    "\n",
    "**Way 2**  \n",
    "Similar to numpy Pandas can accept boolean indexes.  \n",
    "To select rows based on a conditional expression, use a condition inside the selection brackets [].  \n",
    "**Syntax** <code>df[ CONDITION ]</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-1-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3-1-2016</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4-1-2016</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-1-2016</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  maximum temperature  minimum temperature  average temperature  \\\n",
       "1  2-1-2016                   40                   32                 36.0   \n",
       "2  3-1-2016                   45                   35                 40.0   \n",
       "3  4-1-2016                   36                   14                 25.0   \n",
       "4  5-1-2016                   29                   11                 20.0   \n",
       "\n",
       "  precipitation snow fall snow depth  \n",
       "1          0.00       0.0          0  \n",
       "2          0.00       0.0          0  \n",
       "3          0.00       0.0          0  \n",
       "4          0.00       0.0          0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ 1:5 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "361    False\n",
       "362    False\n",
       "363    False\n",
       "364    False\n",
       "365    False\n",
       "Name: maximum temperature, Length: 366, dtype: bool"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"maximum temperature\"] > 95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>23-7-2016</td>\n",
       "      <td>96</td>\n",
       "      <td>80</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>13-8-2016</td>\n",
       "      <td>96</td>\n",
       "      <td>81</td>\n",
       "      <td>88.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  maximum temperature  minimum temperature  average temperature  \\\n",
       "204  23-7-2016                   96                   80                 88.0   \n",
       "225  13-8-2016                   96                   81                 88.5   \n",
       "\n",
       "    precipitation snow fall snow depth  \n",
       "204             0         0          0  \n",
       "225             0         0          0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similar to numpy Pandas can accept boolean indexes\n",
    "df[ df[\"maximum temperature\"] > 95 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the **conditional expression (>, but also ==, !=, <, <=,… would work)** is actually a **pandas Series of boolean values** (either True or False) with the same number of rows as the original DataFrame. Such a `Series` of **boolean values can be used to filter the DataFrame** by putting it in between the selection brackets []. Only **rows for which the value is True will be selected**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10-4-2016</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>10-5-2016</td>\n",
       "      <td>63</td>\n",
       "      <td>50</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>10-6-2016</td>\n",
       "      <td>77</td>\n",
       "      <td>57</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  maximum temperature  minimum temperature  average temperature  \\\n",
       "100  10-4-2016                   50                   31                 40.5   \n",
       "130  10-5-2016                   63                   50                 56.5   \n",
       "161  10-6-2016                   77                   57                 67.0   \n",
       "\n",
       "    precipitation snow fall snow depth  \n",
       "100          0.00       0.0          0  \n",
       "130          0.00       0.0          0  \n",
       "161          0.00       0.0          0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['date'].isin(['10-5-2016', '10-4-2016', '10-6-2016'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the conditional expression, **the isin() conditional function returns a True for each row the values are in the provided list**. To filter the rows based on such a function, use the conditional function inside the selection brackets []. \n",
    "\n",
    "The above is equivalent to filtering by rows for which the date is either '10-5-2016' or '10-4-2016' or '10-6-2016' and combining the three statements with an **| (or) operator**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10-4-2016</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>10-5-2016</td>\n",
       "      <td>63</td>\n",
       "      <td>50</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>10-6-2016</td>\n",
       "      <td>77</td>\n",
       "      <td>57</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  maximum temperature  minimum temperature  average temperature  \\\n",
       "100  10-4-2016                   50                   31                 40.5   \n",
       "130  10-5-2016                   63                   50                 56.5   \n",
       "161  10-6-2016                   77                   57                 67.0   \n",
       "\n",
       "    precipitation snow fall snow depth  \n",
       "100          0.00       0.0          0  \n",
       "130          0.00       0.0          0  \n",
       "161          0.00       0.0          0  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ (df['date']=='10-5-2016') | \n",
    "    (df['date']=='10-4-2016') | \n",
    "    (df['date']=='10-6-2016') \n",
    "  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remember**  \n",
    "When combining multiple conditional statements, **each condition must be surrounded by parentheses ()**. Moreover, you can not use `or`/`and` but need to use the `or` operator `|` and the `and` operator `&`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering specific rows and columns from a `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-1-2016</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3-1-2016</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4-1-2016</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-1-2016</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  maximum temperature  minimum temperature  average temperature  \\\n",
       "1  2-1-2016                   40                   32                 36.0   \n",
       "2  3-1-2016                   45                   35                 40.0   \n",
       "3  4-1-2016                   36                   14                 25.0   \n",
       "4  5-1-2016                   29                   11                 20.0   \n",
       "\n",
       "  precipitation snow fall snow depth  \n",
       "1          0.00       0.0          0  \n",
       "2          0.00       0.0          0  \n",
       "3          0.00       0.0          0  \n",
       "4          0.00       0.0          0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Slicing ?\n",
    "\n",
    "df[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # What if I want a slice of 1 to 4 rows and 2 to 4 cols\n",
    "\n",
    "# df[ 1:5, 'maximum temperature' : 'average temperature' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Turns out to be an InvalidIndexError. Let's try to fix it\n",
    "\n",
    "# df[ 1:5, 1:4 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to resolve this? 😢\n",
    "In case, you want a subset of both rows and columns in one go, just using selection brackets [] is not sufficient anymore.  \n",
    "Here `loc`/`iloc` operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select.\n",
    "\n",
    "**Syntax:**  \n",
    "<code>df.loc[row_label, col_label]</code>  \n",
    "<code>df.iloc[row_index, col_index]</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc() vs iloc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                   10-4-2016\n",
       "maximum temperature           50\n",
       "minimum temperature           31\n",
       "average temperature         40.5\n",
       "precipitation               0.00\n",
       "snow fall                    0.0\n",
       "snow depth                     0\n",
       "Name: 100, dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lable based accessing\n",
    "df.loc[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10-4-2016'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[100, \"date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         10-4-2016\n",
       "snow fall          0.0\n",
       "Name: 100, dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[100, [\"date\", \"snow fall\"] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                   10-4-2016\n",
       "maximum temperature           50\n",
       "minimum temperature           31\n",
       "average temperature         40.5\n",
       "precipitation               0.00\n",
       "snow fall                    0.0\n",
       "snow depth                     0\n",
       "Name: 100, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index based accessing\n",
    "df.iloc[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         10-4-2016\n",
       "snow fall          0.0\n",
       "Name: 100, dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[100, [0, 5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Slicing with Lables\n",
    "\n",
    "# df.loc[ 10:15, \"minimum temperature\":\"precipitation\" ]\n",
    "\n",
    "# # Observe that indexing start from start till end for lable based accessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Slicing with indexes\n",
    "\n",
    "# df.iloc[ 10:15, 2:5 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accessing rows based on a condition**  \n",
    "<code>df.loc[CONDITION , col_lables ]</code>\n",
    "\n",
    "**Accessing rows based on multiple condition**  \n",
    "<code>df.loc[ (COND_1) & (COND_2) | (COND_3) , col_lables ]</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>23-7-2016</td>\n",
       "      <td>96</td>\n",
       "      <td>80</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>13-8-2016</td>\n",
       "      <td>96</td>\n",
       "      <td>81</td>\n",
       "      <td>88.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  maximum temperature  minimum temperature  average temperature  \\\n",
       "204  23-7-2016                   96                   80                 88.0   \n",
       "225  13-8-2016                   96                   81                 88.5   \n",
       "\n",
       "    precipitation snow fall snow depth  \n",
       "204             0         0          0  \n",
       "225             0         0          0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remeber this ?\n",
    "\n",
    "df[ df[\"maximum temperature\"] > 95 ]\n",
    "\n",
    "# Equivalent to filtering rows with max temp greater than 95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204    23-7-2016\n",
       "225    13-8-2016\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What if we want only `dates` with max temp greater than 95 ?\n",
    "\n",
    "df.loc[ df[\"maximum temperature\"] > 95, \"date\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['23-7-2016', '13-8-2016'], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looks like a Series. Can we convert it to a numpy array?\n",
    "\n",
    "df.loc[ df[\"maximum temperature\"] > 95, \"date\" ].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>snow fall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>23-7-2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>13-8-2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date snow fall\n",
       "204  23-7-2016         0\n",
       "225  13-8-2016         0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ df[\"maximum temperature\"] > 95, [\"date\", \"snow fall\"] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225    13-8-2016\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What if we want only `dates` with max temp greater than 95 ?\n",
    "\n",
    "df.loc[ (df[\"maximum temperature\"] > 95) & (df[\"minimum temperature\"] > 80), \"date\" ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renaming Columns, Modifying DataTypes, Creating New Columns and Deleting Columns in `Pandas DataFrame`\n",
    "\n",
    "<img style=\"float: right;\" width=\"300\" height=\"300\" src=\"image/08_newcolumn.PNG\">\n",
    "\n",
    "**Question: How to create new columns derived from existing columns?**  \n",
    "**Answer:** There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward.\n",
    "\n",
    "#### Remember\n",
    "> Create a new column by assigning the output to the DataFrame with a new column name in between the `[]`.  \n",
    "> Operations are element-wise, no need to loop over rows.  \n",
    "> Use `rename()` with a dictionary or function to rename row labels or column names.  \n",
    "> If you need more advanced logic, you can use arbitrary Python code via `apply()`.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading .csv File - Retail Store Sales Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice No</th>\n",
       "      <th>Stock-Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Invoice Date</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice No  Stock-Code                           Description  Quantity  \\\n",
       "0     536365       85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1     536365        71053                  WHITE METAL LANTERN         6   \n",
       "2     536365       84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3     536365       84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4     536365       84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "         Invoice Date  Unit Price  Customer ID         Country  \n",
       "0 2010-12-01 08:26:00        2.55      17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00        3.39      17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00        2.75      17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00        3.39      17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00        3.39      17850.0  United Kingdom  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data/retail_store_sales.xlsx')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Invoice No', ' Stock-Code ', 'Description', 'Quantity', 'Invoice Date',\n",
       "       'Unit Price', 'Customer ID', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What comes to my mind immediately after looking at the dataset?**\n",
    "\n",
    "> 1. How many sales records do we have in the dataset?  \n",
    "> 2. How many customers do we have?  \n",
    "> 3. What is the date range of data?  \n",
    "> 4. Which country recorded maximum sales count?  \n",
    "> 5. What is the minimum order amount and maximum order amount?  \n",
    "> 6. How many orders for each customer?  \n",
    "> 7. What is the revenue contributed by each customer?  \n",
    "> 8. What is the revenue generated each year?  \n",
    "> 9. Which customer contributed to the maximum revenue each year and how much?  \n",
    "> 10. Are there more orders placed on weekends?  \n",
    "> 11. How many customers churned (i.e. Customers not making any purchases for more than or equal to 2 months)?  \n",
    "\n",
    "Try to understand that as a data analyst, first we should be capable to ask right questions. Answering these questions can be done with the help of Pandas module. We will learn later how to answer each of these questions. For now let's understand how to create new columns derived from the existing columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   Invoice No    541909 non-null  object        \n",
      " 1    Stock-Code   541909 non-null  object        \n",
      " 2   Description   540455 non-null  object        \n",
      " 3   Quantity      541909 non-null  int64         \n",
      " 4   Invoice Date  541909 non-null  datetime64[ns]\n",
      " 5   Unit Price    541909 non-null  float64       \n",
      " 6   Customer ID   406829 non-null  float64       \n",
      " 7   Country       541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Sales Record: 541909\n",
      "Total Customers: 4372\n",
      "Date Range: 2010-12-01 08:26:00 to 2011-12-09 12:50:00\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Sales Record:\", df.shape[0])\n",
    "print(\"Total Customers:\", df['Customer ID'].nunique())\n",
    "print(\"Date Range:\", df['Invoice Date'].min(), \"to\", df['Invoice Date'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',\n",
       "       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',\n",
       "       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',\n",
       "       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Austria',\n",
       "       'Israel', 'Finland', 'Bahrain', 'Greece', 'Hong Kong', 'Singapore',\n",
       "       'Lebanon', 'United Arab Emirates', 'Saudi Arabia',\n",
       "       'Czech Republic', 'Canada', 'Unspecified', 'Brazil', 'USA',\n",
       "       'European Community', 'Malta', 'RSA'], dtype=object)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking all the unique countries\n",
    "\n",
    "df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United Kingdom          495478\n",
       "Germany                   9495\n",
       "France                    8557\n",
       "EIRE                      8196\n",
       "Spain                     2533\n",
       "Netherlands               2371\n",
       "Belgium                   2069\n",
       "Switzerland               2002\n",
       "Portugal                  1519\n",
       "Australia                 1259\n",
       "Norway                    1086\n",
       "Italy                      803\n",
       "Channel Islands            758\n",
       "Finland                    695\n",
       "Cyprus                     622\n",
       "Sweden                     462\n",
       "Unspecified                446\n",
       "Austria                    401\n",
       "Denmark                    389\n",
       "Japan                      358\n",
       "Poland                     341\n",
       "Israel                     297\n",
       "USA                        291\n",
       "Hong Kong                  288\n",
       "Singapore                  229\n",
       "Iceland                    182\n",
       "Canada                     151\n",
       "Greece                     146\n",
       "Malta                      127\n",
       "United Arab Emirates        68\n",
       "European Community          61\n",
       "RSA                         58\n",
       "Lebanon                     45\n",
       "Lithuania                   35\n",
       "Brazil                      32\n",
       "Czech Republic              30\n",
       "Bahrain                     19\n",
       "Saudi Arabia                10\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countries with total number of sales record\n",
    "\n",
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Columns\n",
    "**Syntax to rename columns**  \n",
    "<code>df.rename(index=None, columns=None)</code>\n",
    "\n",
    "The `rename()` function can be used for both row labels and column labels. Provide a dictionary with the keys the current names and the values the new names to update the corresponding names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Invoice No', ' Stock-Code ', 'Description', 'Quantity', 'Invoice Date',\n",
       "       'Unit Price', 'Customer ID', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Invoice No', ' Stock-Code ', 'Product Description', 'Quantity',\n",
       "       'Invoice Date', 'Unit Price', 'Cust ID', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed = df.rename(columns={'Description': 'Product Description', 'Customer ID': 'Cust ID'})\n",
    "\n",
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice No</th>\n",
       "      <th>Stock-Code</th>\n",
       "      <th>Product Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Invoice Date</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Cust ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice No  Stock-Code                   Product Description  Quantity  \\\n",
       "0     536365       85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1     536365        71053                  WHITE METAL LANTERN         6   \n",
       "2     536365       84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3     536365       84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4     536365       84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "         Invoice Date  Unit Price  Cust ID         Country  \n",
       "0 2010-12-01 08:26:00        2.55  17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00        3.39  17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00        2.75  17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00        3.39  17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00        3.39  17850.0  United Kingdom  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A very common column renaming strategy**  \n",
    "Let's convert column names by performing below mentioned operations:\n",
    "> 1. Strip extra spaces  \n",
    "> 2. Convert to lower cases  \n",
    "> 3. Remove all the special characters including spaces  \n",
    "\n",
    "\n",
    "Benifit of this is, we can now access the columns in the dataframe using the dot, similar to how we access the properties/attributes of a python object. For eg:  \n",
    "**Acessing INVOICE NO can be done using:** <code>df_renamed.invoice_no</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['invoice_no', 'stock_code', 'product_description', 'quantity', 'invoice_date', 'unit_price', 'cust_id', 'country']\n"
     ]
    }
   ],
   "source": [
    "col_names = [ col.strip().lower().replace(' ', '_').replace('-', '_') for col in df_renamed.columns ]\n",
    "\n",
    "print(col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Invoice No', ' Stock-Code ', 'Product Description', 'Quantity',\n",
       "       'Invoice Date', 'Unit Price', 'Cust ID', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns = col_names\n",
    "\n",
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying Columns DataType\n",
    "\n",
    "**Modifying the DataType using DataFrame.astype()**  \n",
    "We can pass any `Python`, `Numpy`, or `Pandas` datatype to change all columns of a Dataframe to that type, or we can pass a dictionary having column names as keys and datatype as values to change the type of selected columns. \n",
    "\n",
    "**Modifying the DataType using DataFrame.apply()**  \n",
    "We can pass `pandas.to_numeric`, `pandas.to_datetime`, and `pandas.to_timedelta` as arguments to apply the `apply()` function to change the data type of one or more columns to numeric, DateTime, and time delta respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modifying the DataType using DataFrame.astype()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "invoice_no             object\n",
       "stock_code             object\n",
       "product_description    object\n",
       "quantity               object\n",
       "invoice_date           object\n",
       "unit_price             object\n",
       "cust_id                object\n",
       "country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting all columns to string type\n",
    "df_renamed = df_renamed.astype(str)\n",
    "\n",
    "df_renamed.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "invoice_no              object\n",
       "stock_code              object\n",
       "product_description     object\n",
       "quantity               float64\n",
       "invoice_date            object\n",
       "unit_price             float64\n",
       "cust_id                float64\n",
       "country                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed[['quantity', 'unit_price', 'cust_id']] = df_renamed[['quantity', 'unit_price', 'cust_id']].astype(float)\n",
    "\n",
    "df_renamed.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "invoice_no              object\n",
       "stock_code              object\n",
       "product_description     object\n",
       "quantity                 int32\n",
       "invoice_date            object\n",
       "unit_price             float64\n",
       "cust_id                float64\n",
       "country                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using dictionary to convert specific columns\n",
    "convert_dict = {'quantity': int,\n",
    "                'country': str\n",
    "                }\n",
    " \n",
    "df_renamed = df_renamed.astype(convert_dict)\n",
    "\n",
    "df_renamed.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modifying the DataType using DataFrame.apply()**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "invoice_no                     object\n",
       "stock_code                     object\n",
       "product_description            object\n",
       "quantity                        int32\n",
       "invoice_date           datetime64[ns]\n",
       "unit_price                    float64\n",
       "cust_id                       float64\n",
       "country                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using apply method to convert datatype\n",
    "\n",
    "df_renamed['invoice_date'] = df_renamed['invoice_date'].apply(pd.to_datetime)\n",
    "\n",
    "df_renamed.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Derived Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_no</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>product_description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_no stock_code                  product_description  quantity  \\\n",
       "0     536365     85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1     536365      71053                  WHITE METAL LANTERN         6   \n",
       "2     536365     84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3     536365     84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4     536365     84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "         invoice_date  unit_price  cust_id         country  amount  \n",
       "0 2010-12-01 08:26:00        2.55  17850.0  United Kingdom   15.30  \n",
       "1 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34  \n",
       "2 2010-12-01 08:26:00        2.75  17850.0  United Kingdom   22.00  \n",
       "3 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34  \n",
       "4 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a column by merging Product Category and Sub-category\n",
    "# Think about how to perform the same operation in Numpy?\n",
    "\n",
    "df_renamed['amount'] = df_renamed['quantity'] * df_renamed['unit_price']\n",
    "\n",
    "df_renamed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remember**  \n",
    "The calculation is again element-wise, so the `+` is applied for the values in each row. Also other mathematical operators (+, -, *, /,…) or logical operators (<, >, ==,…) work element-wise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Columns using apply() function\n",
    "**Syntax for DataFrame**  \n",
    "<code>df.apply(function, axis=0)</code>  \n",
    "Applies the `function` column wise.  \n",
    "**Axis Parameter**  \n",
    "Axis along which the function is applied. Axis can be {0 or ‘index’, 1 or ‘columns’}, default 0:\n",
    "- 0 or ‘index’: apply function to each column.\n",
    "- 1 or ‘columns’: apply function to each row.\n",
    "\n",
    "**Syntax for Series**  \n",
    "<code>series.apply(function, axis=0)</code>  \n",
    "Applies the function element wise.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "invoice_no                     object\n",
       "stock_code                     object\n",
       "product_description            object\n",
       "quantity                        int32\n",
       "invoice_date           datetime64[ns]\n",
       "unit_price                    float64\n",
       "cust_id                       float64\n",
       "country                        object\n",
       "amount                        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "invoice_no                         C581569\n",
       "stock_code                               m\n",
       "product_description      wrongly sold sets\n",
       "quantity                             80995\n",
       "invoice_date           2011-12-09 12:50:00\n",
       "unit_price                         38970.0\n",
       "cust_id                            18287.0\n",
       "country                        Unspecified\n",
       "amount                            168469.6\n",
       "dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.max function is applied column wise by default - i.e. axis=0\n",
    "\n",
    "df_renamed.apply(np.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount    17.987795\n",
       "dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply a function on the complete column at once\n",
    "\n",
    "df_renamed[['amount']].apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.987794877005495"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is much better way of performing above operation - df['order_amount'].mean()\n",
    "\n",
    "df_renamed['amount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         15.30\n",
       "1         20.34\n",
       "2         22.00\n",
       "3         20.34\n",
       "4         20.34\n",
       "          ...  \n",
       "541904    10.20\n",
       "541905    12.60\n",
       "541906    16.60\n",
       "541907    16.60\n",
       "541908    14.85\n",
       "Name: amount, Length: 541909, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply a function on the column - row wise. Returns Series.\n",
    "\n",
    "df_renamed['amount'].apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_no</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>product_description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>amount</th>\n",
       "      <th>new_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_no stock_code                  product_description  quantity  \\\n",
       "0     536365     85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1     536365      71053                  WHITE METAL LANTERN         6   \n",
       "2     536365     84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3     536365     84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4     536365     84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "         invoice_date  unit_price  cust_id         country  amount  new_amount  \n",
       "0 2010-12-01 08:26:00        2.55  17850.0  United Kingdom   15.30       15.30  \n",
       "1 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34       20.34  \n",
       "2 2010-12-01 08:26:00        2.75  17850.0  United Kingdom   22.00       22.00  \n",
       "3 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34       20.34  \n",
       "4 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34       20.34  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new column using apply()\n",
    "# Let's assume we have to create a column - new_amount\n",
    "# new_amount = quantity * unit_price\n",
    "# we already saw how to perform this using df['amount'] = df['quantity'] * df['unit_price']\n",
    "# Let's do the same operation using apply() function now\n",
    "\n",
    "df_renamed['new_amount'] = df_renamed.apply(lambda row: row['quantity'] * row['unit_price'], axis=1)\n",
    "\n",
    "df_renamed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_no</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>product_description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>amount</th>\n",
       "      <th>new_amount</th>\n",
       "      <th>new_amount_with_taxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>15.30</td>\n",
       "      <td>18.0540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_no stock_code                  product_description  quantity  \\\n",
       "0     536365     85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1     536365      71053                  WHITE METAL LANTERN         6   \n",
       "2     536365     84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3     536365     84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4     536365     84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "         invoice_date  unit_price  cust_id         country  amount  \\\n",
       "0 2010-12-01 08:26:00        2.55  17850.0  United Kingdom   15.30   \n",
       "1 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34   \n",
       "2 2010-12-01 08:26:00        2.75  17850.0  United Kingdom   22.00   \n",
       "3 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34   \n",
       "4 2010-12-01 08:26:00        3.39  17850.0  United Kingdom   20.34   \n",
       "\n",
       "   new_amount  new_amount_with_taxes  \n",
       "0       15.30                18.0540  \n",
       "1       20.34                24.0012  \n",
       "2       22.00                25.9600  \n",
       "3       20.34                24.0012  \n",
       "4       20.34                24.0012  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a column new_amount_with_taxes\n",
    "# Let's assume an 18% tax on each product\n",
    "# This can be done using df['new_amount_with_taxes'] = df['amount'] * 1.18\n",
    "\n",
    "df_renamed['new_amount_with_taxes'] = df_renamed['new_amount'].apply(lambda col: col * 1.18)\n",
    "\n",
    "df_renamed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting column(s) in DataFrame\n",
    "\n",
    "**Syntax 1 - Dropping columns by using columns name**  \n",
    "```python\n",
    "# Dropping two columns by passing column names\n",
    "# inplace=True parameter performs the operation saves the result back to the dataframe\n",
    "df.drop(['col1', 'col3'], axis=1, inplace=True)\n",
    "```\n",
    "\n",
    "**Syntax 2 - Removing columns by using columns name using loc[]**  \n",
    "```python\n",
    "# Removing all columns between col2 to col4\n",
    "df.drop(df.loc[:, 'col2':'col4'], inplace=True, axis=1)\n",
    "```\n",
    "\n",
    "**Syntax 3 - Removing column based on index**  \n",
    "```python\n",
    "# Remove three columns as index base\n",
    "df.drop(df.columns[[0, 4, 2]], axis=1, inplace=True)\n",
    "```\n",
    "\n",
    "**Syntax 4 - Removing column based on index using iloc[]**\n",
    "```python\n",
    "# removing two columns between column index 1 to 3\n",
    "df.drop(df.iloc[:, 1:3], inplace=True, axis=1)\n",
    "```\n",
    "\n",
    "**Synatx 5 - DataFrame.pop() method**  \n",
    "```python\n",
    "# Using pop() we can delete single column at a time\n",
    "df.pop(\"Col4\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country', 'amount',\n",
       "       'new_amount', 'new_amount_with_taxes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_no</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>product_description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>new_amount</th>\n",
       "      <th>new_amount_with_taxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>18.0540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>10.20</td>\n",
       "      <td>12.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.60</td>\n",
       "      <td>14.8680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>14.85</td>\n",
       "      <td>17.5230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       invoice_no stock_code                  product_description  quantity  \\\n",
       "0          536365     85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1          536365      71053                  WHITE METAL LANTERN         6   \n",
       "2          536365     84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3          536365     84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4          536365     84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "...           ...        ...                                  ...       ...   \n",
       "541904     581587      22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905     581587      22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906     581587      23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907     581587      23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908     581587      22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "              invoice_date  unit_price  cust_id         country  new_amount  \\\n",
       "0      2010-12-01 08:26:00        2.55  17850.0  United Kingdom       15.30   \n",
       "1      2010-12-01 08:26:00        3.39  17850.0  United Kingdom       20.34   \n",
       "2      2010-12-01 08:26:00        2.75  17850.0  United Kingdom       22.00   \n",
       "3      2010-12-01 08:26:00        3.39  17850.0  United Kingdom       20.34   \n",
       "4      2010-12-01 08:26:00        3.39  17850.0  United Kingdom       20.34   \n",
       "...                    ...         ...      ...             ...         ...   \n",
       "541904 2011-12-09 12:50:00        0.85  12680.0          France       10.20   \n",
       "541905 2011-12-09 12:50:00        2.10  12680.0          France       12.60   \n",
       "541906 2011-12-09 12:50:00        4.15  12680.0          France       16.60   \n",
       "541907 2011-12-09 12:50:00        4.15  12680.0          France       16.60   \n",
       "541908 2011-12-09 12:50:00        4.95  12680.0          France       14.85   \n",
       "\n",
       "        new_amount_with_taxes  \n",
       "0                     18.0540  \n",
       "1                     24.0012  \n",
       "2                     25.9600  \n",
       "3                     24.0012  \n",
       "4                     24.0012  \n",
       "...                       ...  \n",
       "541904                12.0360  \n",
       "541905                14.8680  \n",
       "541906                19.5880  \n",
       "541907                19.5880  \n",
       "541908                17.5230  \n",
       "\n",
       "[541909 rows x 10 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Syntax 1\n",
    "\n",
    "df_renamed.drop(['amount'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country', 'amount',\n",
       "       'new_amount', 'new_amount_with_taxes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**  \n",
    "Observe that the `amount` column is still not removed from dataframe. To make the changes permanent, pass `inplace=True` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_renamed.drop(['amount'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country', 'new_amount',\n",
       "       'new_amount_with_taxes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>new_amount</th>\n",
       "      <th>new_amount_with_taxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>18.0540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>10.20</td>\n",
       "      <td>12.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.60</td>\n",
       "      <td>14.8680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>14.85</td>\n",
       "      <td>17.5230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        unit_price  cust_id         country  new_amount  new_amount_with_taxes\n",
       "0             2.55  17850.0  United Kingdom       15.30                18.0540\n",
       "1             3.39  17850.0  United Kingdom       20.34                24.0012\n",
       "2             2.75  17850.0  United Kingdom       22.00                25.9600\n",
       "3             3.39  17850.0  United Kingdom       20.34                24.0012\n",
       "4             3.39  17850.0  United Kingdom       20.34                24.0012\n",
       "...            ...      ...             ...         ...                    ...\n",
       "541904        0.85  12680.0          France       10.20                12.0360\n",
       "541905        2.10  12680.0          France       12.60                14.8680\n",
       "541906        4.15  12680.0          France       16.60                19.5880\n",
       "541907        4.15  12680.0          France       16.60                19.5880\n",
       "541908        4.95  12680.0          France       14.85                17.5230\n",
       "\n",
       "[541909 rows x 5 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Syntax 2\n",
    "\n",
    "df_renamed.drop(df_renamed.loc[:, 'invoice_no':'invoice_date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stock_code</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>new_amount</th>\n",
       "      <th>new_amount_with_taxes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85123A</td>\n",
       "      <td>6</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>18.0540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71053</td>\n",
       "      <td>6</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84406B</td>\n",
       "      <td>8</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84029G</td>\n",
       "      <td>6</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84029E</td>\n",
       "      <td>6</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>22613</td>\n",
       "      <td>12</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>10.20</td>\n",
       "      <td>12.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>22899</td>\n",
       "      <td>6</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.60</td>\n",
       "      <td>14.8680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>23254</td>\n",
       "      <td>4</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>23255</td>\n",
       "      <td>4</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>22138</td>\n",
       "      <td>3</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>14.85</td>\n",
       "      <td>17.5230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stock_code  quantity  unit_price  cust_id         country  new_amount  \\\n",
       "0          85123A         6        2.55  17850.0  United Kingdom       15.30   \n",
       "1           71053         6        3.39  17850.0  United Kingdom       20.34   \n",
       "2          84406B         8        2.75  17850.0  United Kingdom       22.00   \n",
       "3          84029G         6        3.39  17850.0  United Kingdom       20.34   \n",
       "4          84029E         6        3.39  17850.0  United Kingdom       20.34   \n",
       "...           ...       ...         ...      ...             ...         ...   \n",
       "541904      22613        12        0.85  12680.0          France       10.20   \n",
       "541905      22899         6        2.10  12680.0          France       12.60   \n",
       "541906      23254         4        4.15  12680.0          France       16.60   \n",
       "541907      23255         4        4.15  12680.0          France       16.60   \n",
       "541908      22138         3        4.95  12680.0          France       14.85   \n",
       "\n",
       "        new_amount_with_taxes  \n",
       "0                     18.0540  \n",
       "1                     24.0012  \n",
       "2                     25.9600  \n",
       "3                     24.0012  \n",
       "4                     24.0012  \n",
       "...                       ...  \n",
       "541904                12.0360  \n",
       "541905                14.8680  \n",
       "541906                19.5880  \n",
       "541907                19.5880  \n",
       "541908                17.5230  \n",
       "\n",
       "[541909 rows x 7 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Syntax 3\n",
    "\n",
    "df_renamed.drop(df_renamed.columns[[0, 4, 2]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country', 'new_amount',\n",
       "       'new_amount_with_taxes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**  \n",
    "Observe that the columns are still not removed from dataframe. To make the changes permanent, pass `inplace=True` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_no</th>\n",
       "      <th>quantity</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>country</th>\n",
       "      <th>new_amount</th>\n",
       "      <th>new_amount_with_taxes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>18.0540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
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       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>24.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>10.20</td>\n",
       "      <td>12.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.60</td>\n",
       "      <td>14.8680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>19.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>14.85</td>\n",
       "      <td>17.5230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       invoice_no  quantity        invoice_date  unit_price  cust_id  \\\n",
       "0          536365         6 2010-12-01 08:26:00        2.55  17850.0   \n",
       "1          536365         6 2010-12-01 08:26:00        3.39  17850.0   \n",
       "2          536365         8 2010-12-01 08:26:00        2.75  17850.0   \n",
       "3          536365         6 2010-12-01 08:26:00        3.39  17850.0   \n",
       "4          536365         6 2010-12-01 08:26:00        3.39  17850.0   \n",
       "...           ...       ...                 ...         ...      ...   \n",
       "541904     581587        12 2011-12-09 12:50:00        0.85  12680.0   \n",
       "541905     581587         6 2011-12-09 12:50:00        2.10  12680.0   \n",
       "541906     581587         4 2011-12-09 12:50:00        4.15  12680.0   \n",
       "541907     581587         4 2011-12-09 12:50:00        4.15  12680.0   \n",
       "541908     581587         3 2011-12-09 12:50:00        4.95  12680.0   \n",
       "\n",
       "               country  new_amount  new_amount_with_taxes  \n",
       "0       United Kingdom       15.30                18.0540  \n",
       "1       United Kingdom       20.34                24.0012  \n",
       "2       United Kingdom       22.00                25.9600  \n",
       "3       United Kingdom       20.34                24.0012  \n",
       "4       United Kingdom       20.34                24.0012  \n",
       "...                ...         ...                    ...  \n",
       "541904          France       10.20                12.0360  \n",
       "541905          France       12.60                14.8680  \n",
       "541906          France       16.60                19.5880  \n",
       "541907          France       16.60                19.5880  \n",
       "541908          France       14.85                17.5230  \n",
       "\n",
       "[541909 rows x 8 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Syntax 4\n",
    "\n",
    "df_renamed.drop(df_renamed.iloc[:, 1:3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country', 'new_amount',\n",
       "       'new_amount_with_taxes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**  \n",
    "Observe that the columns are still not removed from dataframe. To make the changes permanent, pass `inplace=True` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         15.30\n",
       "1         20.34\n",
       "2         22.00\n",
       "3         20.34\n",
       "4         20.34\n",
       "          ...  \n",
       "541904    10.20\n",
       "541905    12.60\n",
       "541906    16.60\n",
       "541907    16.60\n",
       "541908    14.85\n",
       "Name: new_amount, Length: 541909, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Syntax 5\n",
    "\n",
    "df_renamed.pop(\"new_amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_no', 'stock_code', 'product_description', 'quantity',\n",
       "       'invoice_date', 'unit_price', 'cust_id', 'country',\n",
       "       'new_amount_with_taxes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_renamed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remeber that DataFrame.pop(\"Col_Name\") function:**  \n",
    "> 1. Removes the single column and returns the deleted column.\n",
    "> 2. Applies the changes to the dataframe without any need of `inplace=True`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding/Inserting Row(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading a .xlsx File - Weather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"data/weather_data.xlsx\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insert Row(s) using Dictionary - pandas.concat()\n",
    "\n",
    "**Syntax 1 - Inserting a Single Row**  \n",
    "```python\n",
    "# Creat a new record using Dictionary\n",
    "new_record = pd.DataFrame([{'day': '1/7/2017', 'temperature': 36, 'windspeed': 4, 'event': 'Sunny'}])\n",
    "\n",
    "# Inserting row at the end\n",
    "df = pd.concat([df, new_record], ignore_index=True)\n",
    "\n",
    "# Inserting row at the top\n",
    "df = pd.concat([new_record, df], ignore_index=True)\n",
    "```\n",
    "\n",
    "**Syntax 2 - Insert multiple rows (i.e. a batch of data)**  \n",
    "```python\n",
    "# Creat a new record using Dictionary\n",
    "batch_records = pd.DataFrame([{'day': '1/8/2017', 'temperature': 30, 'windspeed': 3, 'event': 'Rain'}, {'day': '1/9/2017', 'temperature': 27, 'windspeed': 4, 'event': 'Snow'}])\n",
    "\n",
    "# Inserting row at the end\n",
    "df = pd.concat([df, batch_records], ignore_index=True)\n",
    "\n",
    "# Inserting row at the top\n",
    "df = pd.concat([batch_records, df], ignore_index=True)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/7/2017</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/7/2017           36          4  Sunny"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creat a new record using Dictionary\n",
    "new_record = pd.DataFrame([{'day': '1/7/2017', \n",
    "                            'temperature': 36, \n",
    "                            'windspeed': 4, \n",
    "                            'event': 'Sunny'}])\n",
    "\n",
    "# Inserting row at the end\n",
    "df = pd.concat([df, new_record], ignore_index=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/7/2017</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/7/2017           36          4  Sunny\n",
       "7  1/8/2017           30          3   Rain\n",
       "8  1/9/2017           27          4   Snow"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creat a new record using Dictionary\n",
    "batch_records = pd.DataFrame([{'day': '1/8/2017', 'temperature': 30, 'windspeed': 3, 'event': 'Rain'}, \n",
    "                              {'day': '1/9/2017', 'temperature': 27, 'windspeed': 4, 'event': 'Snow'}])\n",
    "\n",
    "# Inserting row at the end\n",
    "df = pd.concat([df, batch_records], ignore_index=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting a Row using List - .loc[] and .iloc[]\n",
    "\n",
    "To add a list to a Pandas DataFrame works a bit differently since we can’t simply use the `.concat()` function. In order to do this, we need to use the `loc accessor`. The label that we use for our `loc` \n",
    "accessor will be the length of the DataFrame. This will create a new row.\n",
    "\n",
    "**Syntax - Using DataFrame.loc[]**  \n",
    "```python\n",
    "df.loc[len(df)] = ['1/12/2017', 28, 2, 'Rain']\n",
    "```\n",
    "\n",
    "**Syntax - Using DataFrame.iloc[]**  \n",
    "**Generates Error - You cannot use .iloc to enlarge the target object.(i.e .iloc can't be used to add new rows)** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/7/2017</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/12/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed  event\n",
       "0   1/1/2017           32          6   Rain\n",
       "1   1/2/2017           35          7  Sunny\n",
       "2   1/3/2017           28          2   Snow\n",
       "3   1/4/2017           24          7   Snow\n",
       "4   1/5/2017           32          4   Rain\n",
       "5   1/6/2017           31          2  Sunny\n",
       "6   1/7/2017           36          4  Sunny\n",
       "7   1/8/2017           30          3   Rain\n",
       "8   1/9/2017           27          4   Snow\n",
       "9  1/12/2017           28          2   Rain"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[len(df)] = ['1/12/2017', 28, 2, 'Rain']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "iloc cannot enlarge its target object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16792\\731336179.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'1/13/2017'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Rain'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    711\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_setitem_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 713\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_setitem_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_setitem_indexer\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m   1414\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1415\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1416\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"iloc cannot enlarge its target object\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1417\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1418\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"iloc cannot enlarge its target object\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: iloc cannot enlarge its target object"
     ]
    }
   ],
   "source": [
    "df.iloc[len(df)] = ['1/13/2017', 30, 3, 'Rain']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting a Row at a Specific Index of a DataFrame\n",
    "Adding a row at a specific index is a bit different. As shown in the example of using lists, we need to use the `loc accessor`. **However, inserting a row at a given index will only overwrite this**. What we can do instead is pass in a value close to where we want to insert the new row.\n",
    "\n",
    "For example, if we have current indices from 0-9 and we want to insert a new row at index 9, we can simply assign it using index 8.5. Let’s see how this works:\n",
    "\n",
    "**Syntax - Inserting a row at a specific index**  \n",
    "```python\n",
    "# Adding at row label 8.5\n",
    "df.loc[8.5] = ['1/11/2017', 30, 3, 'Rain']\n",
    "\n",
    "# sort index\n",
    "df = df.sort_index().reset_index(drop=True)\n",
    "\n",
    "df\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>Snow</td>\n",
       "    </tr>\n",
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       "      <td>28</td>\n",
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       "      <td>Rain</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed  event\n",
       "0    1/1/2017           32          6   Rain\n",
       "1    1/2/2017           35          7  Sunny\n",
       "2    1/3/2017           28          2   Snow\n",
       "3    1/4/2017           24          7   Snow\n",
       "4    1/5/2017           32          4   Rain\n",
       "5    1/6/2017           31          2  Sunny\n",
       "6    1/7/2017           36          4  Sunny\n",
       "7    1/8/2017           30          3   Rain\n",
       "8    1/9/2017           27          4   Snow\n",
       "9   1/10/2017           30          3   Rain\n",
       "10  1/12/2017           28          2   Rain"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding at row label 8.5\n",
    "df.loc[8.5] = ['1/10/2017', 30, 3, 'Rain']\n",
    "\n",
    "#sort index\n",
    "df = df.sort_index().reset_index(drop=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          day  temperature  windspeed  event\n",
       "0    1/1/2017           32          6   Rain\n",
       "1    1/2/2017           35          7  Sunny\n",
       "2    1/3/2017           28          2   Snow\n",
       "3    1/4/2017           24          7   Snow\n",
       "4    1/5/2017           32          4   Rain\n",
       "5    1/6/2017           31          2  Sunny\n",
       "6    1/7/2017           36          4  Sunny\n",
       "7    1/8/2017           30          3   Rain\n",
       "8    1/9/2017           27          4   Snow\n",
       "9   1/10/2017           30          3   Rain\n",
       "10  1/11/2017           27          1   Snow\n",
       "11  1/12/2017           28          2   Rain"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding at row label 9.5\n",
    "df.loc[9.5] = ['1/11/2017', 27, 1, 'Snow']\n",
    "\n",
    "#sort index\n",
    "df = df.sort_index().reset_index(drop=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving DataFrame to .xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('data/temp/updated_weather_data.xlsx', sheet_name='weather_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling TimeSeries Data\n",
    "\n",
    "**Question: How to handle time series data?**  \n",
    "**Answer:** pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data.\n",
    "\n",
    "\n",
    "#### Remember\n",
    "> Valid date strings can be converted to datetime objects using `to_datetime` function or as part of read functions.  \n",
    "> `pandas.Datetime` objects in pandas support calculations, logical operations and convenient date-related properties using the `dt` accessor like `year`, `month`, `day`, `day_of_week`, `day_of_year`, `is_leap_year`, `week`, etc...  \n",
    "> We can also access `datetime` methods using `dt` accessor like `day_name()`, `month_name()`, etc...  \n",
    "> `pandas.Timedelta` Represents a duration, the difference between two dates or times. Many properties of timedelta can be accessed using `dt` like `components`, `days`, `seconds`, etc...  \n",
    "> We can also access `timedelta` methods using `dt` accessor like `total_seconds()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading .csv File - Online Store Sales Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
       "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/online_store_sales.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Row ID         9800 non-null   int64  \n",
      " 1   Order ID       9800 non-null   object \n",
      " 2   Order Date     9800 non-null   object \n",
      " 3   Ship Date      9800 non-null   object \n",
      " 4   Ship Mode      9800 non-null   object \n",
      " 5   Customer ID    9800 non-null   object \n",
      " 6   Customer Name  9800 non-null   object \n",
      " 7   Segment        9800 non-null   object \n",
      " 8   Country        9800 non-null   object \n",
      " 9   City           9800 non-null   object \n",
      " 10  State          9800 non-null   object \n",
      " 11  Postal Code    9789 non-null   float64\n",
      " 12  Region         9800 non-null   object \n",
      " 13  Product ID     9800 non-null   object \n",
      " 14  Category       9800 non-null   object \n",
      " 15  Sub-Category   9800 non-null   object \n",
      " 16  Product Name   9800 non-null   object \n",
      " 17  Sales          9800 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(15)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What comes to my mind immediately after looking at the dataset?**\n",
    "\n",
    "> 1. What are the different customer segments?  \n",
    "> 2. How many sales records do we have in the dataset?  \n",
    "> 3. Which region recorded maximum sales count?  \n",
    "> 4. What are the different product categories?  \n",
    "> 5. What is the minimum order amount and maximum order amount?  \n",
    "> 6. What is the revenue generated in the year 2017?  \n",
    "> 7. Which customer contributed to the maximum revenue in 2017 and how much?  \n",
    "> 8. Which product category is doing best? (revenue and count)  \n",
    "> 9. Are there more orders placed on weekends?  \n",
    "> 10. How many days on average it takes for the products to get shipped? \n",
    "\n",
    "Try to understand that as a data analyst, first we should be capable to ask right questions. Answering these questions can be done with the help of Pandas module. We will learn later how to answer each of these questions. For now let's understand how to create new columns derived from the existing columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Row ID         9800 non-null   int64  \n",
      " 1   Order ID       9800 non-null   object \n",
      " 2   Order Date     9800 non-null   object \n",
      " 3   Ship Date      9800 non-null   object \n",
      " 4   Ship Mode      9800 non-null   object \n",
      " 5   Customer ID    9800 non-null   object \n",
      " 6   Customer Name  9800 non-null   object \n",
      " 7   Segment        9800 non-null   object \n",
      " 8   Country        9800 non-null   object \n",
      " 9   City           9800 non-null   object \n",
      " 10  State          9800 non-null   object \n",
      " 11  Postal Code    9789 non-null   float64\n",
      " 12  Region         9800 non-null   object \n",
      " 13  Product ID     9800 non-null   object \n",
      " 14  Category       9800 non-null   object \n",
      " 15  Sub-Category   9800 non-null   object \n",
      " 16  Product Name   9800 non-null   object \n",
      " 17  Sales          9800 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(15)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pd.to_datetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/05/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/05/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/03/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/06/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/05/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/02/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/02/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/04/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/02/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/04/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/06/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/04/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/01/2015' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/02/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/01/2018' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/06/2017' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/01/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\Kanav\\Anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/05/2016' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>2017-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>2017-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-06</td>\n",
       "      <td>2017-06-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>2016-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>2016-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9795</th>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>2017-05-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9796</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2016-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9797</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2016-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9798</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2016-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9799</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2016-01-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9800 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order Date  Ship Date\n",
       "0    2017-08-11 2017-11-11\n",
       "1    2017-08-11 2017-11-11\n",
       "2    2017-12-06 2017-06-16\n",
       "3    2016-11-10 2016-10-18\n",
       "4    2016-11-10 2016-10-18\n",
       "...         ...        ...\n",
       "9795 2017-05-21 2017-05-28\n",
       "9796 2016-12-01 2016-01-17\n",
       "9797 2016-12-01 2016-01-17\n",
       "9798 2016-12-01 2016-01-17\n",
       "9799 2016-12-01 2016-01-17\n",
       "\n",
       "[9800 rows x 2 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Order Date', 'Ship Date']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**These many warnings! Let's learn how to handle them? 🥵**  \n",
    "These warnings are generated for a reason. Since dates can be specified in various formats, for eg: DD/MM/YYYY or YYYY/MM/DD or MM/DD/YYYY etc...  \n",
    "\n",
    "Here pandas is generating these warnings to warn you to **specify a format(of how dates are stored in the datetime column)** so that you can prevent any Parsing error in future.  \n",
    "\n",
    "There are two ways to get rid of these warnings:  \n",
    "**Way 1** Add parameter `dayfirst=True`   \n",
    "**Way 2** Add parameter `format=\"%d/%m/%Y\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2017-11-11\n",
       "1      2017-11-11\n",
       "2      2017-06-16\n",
       "3      2016-10-18\n",
       "4      2016-10-18\n",
       "          ...    \n",
       "9795   2017-05-28\n",
       "9796   2016-01-17\n",
       "9797   2016-01-17\n",
       "9798   2016-01-17\n",
       "9799   2016-01-17\n",
       "Name: Ship Date, Length: 9800, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df['Ship Date'], dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2017-11-11\n",
       "1      2017-11-11\n",
       "2      2017-06-16\n",
       "3      2016-10-18\n",
       "4      2016-10-18\n",
       "          ...    \n",
       "9795   2017-05-28\n",
       "9796   2016-01-17\n",
       "9797   2016-01-17\n",
       "9798   2016-01-17\n",
       "9799   2016-01-17\n",
       "Name: Ship Date, Length: 9800, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df['Ship Date'], format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Row ID         9800 non-null   int64         \n",
      " 1   Order ID       9800 non-null   object        \n",
      " 2   Order Date     9800 non-null   datetime64[ns]\n",
      " 3   Ship Date      9800 non-null   datetime64[ns]\n",
      " 4   Ship Mode      9800 non-null   object        \n",
      " 5   Customer ID    9800 non-null   object        \n",
      " 6   Customer Name  9800 non-null   object        \n",
      " 7   Segment        9800 non-null   object        \n",
      " 8   Country        9800 non-null   object        \n",
      " 9   City           9800 non-null   object        \n",
      " 10  State          9800 non-null   object        \n",
      " 11  Postal Code    9789 non-null   float64       \n",
      " 12  Region         9800 non-null   object        \n",
      " 13  Product ID     9800 non-null   object        \n",
      " 14  Category       9800 non-null   object        \n",
      " 15  Sub-Category   9800 non-null   object        \n",
      " 16  Product Name   9800 non-null   object        \n",
      " 17  Sales          9800 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(2), int64(1), object(13)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df['Ship Date'] = pd.to_datetime(df['Ship Date'], format=\"%d/%m/%Y\")\n",
    "df['Order Date'] = pd.to_datetime(df['Order Date'], format=\"%d/%m/%Y\")\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, the values in `Order Date` and `Ship Date` were character strings and do not provide any datetime operations (e.g. extract the year, day of the week,…). By applying the `to_datetime` function, pandas interprets the strings and convert these to datetime (i.e. `datetime64[ns, UTC]`) objects.  \n",
    "\n",
    "**Important Note**  \n",
    "As many data sets do contain datetime information in one of the columns, pandas input function like `pandas.read_csv()` and `pandas.read_json()` can do the transformation to dates when reading the data using the `parse_dates` parameter with a list of the columns to read as Timestamp:  \n",
    "<code>pd.read_csv(PATH, parse_dates=[\"cols\"])</code>\n",
    "\n",
    "Remember, the warnings while parsing dates?  \n",
    "You can fix those warnings by passing either one of the two parameters: `dayfirst=True` or `date_format`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3       4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/online_store_sales.csv', parse_dates=[\"Order Date\", \"Ship Date\"], dayfirst=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['row_id', 'order_id', 'order_date', 'ship_date', 'ship_mode',\n",
       "       'customer_id', 'customer_name', 'segment', 'country', 'city', 'state',\n",
       "       'postal_code', 'region', 'product_id', 'category', 'sub_category',\n",
       "       'product_name', 'sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = [ col.strip().lower().replace(' ', '_').replace('-', '_') for col in df.columns ]\n",
    "\n",
    "df.columns = col_names\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-01-03 00:00:00')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_date'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orders starting from 2015-01-03 00:00:00 till 2018-12-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(\"Orders starting from\", df['order_date'].min(), \"till\", df['order_date'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1457 days 00:00:00')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_date'].max() - df['order_date'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with DateTime in Pandas\n",
    "\n",
    "#### Get year, month, and day\n",
    "\n",
    "```python\n",
    "df['year']= df['DoB'].dt.year\n",
    "df['month']= df['DoB'].dt.month\n",
    "df['day']= df['DoB'].dt.day\n",
    "```\n",
    "\n",
    "#### Get the week of year, the day of week and leap year\n",
    "```python\n",
    "df['week_of_year'] = df['DoB'].dt.week\n",
    "df['day_of_week'] = df['DoB'].dt.dayofweek\n",
    "df['is_leap_year'] = df['DoB'].dt.is_leap_year\n",
    "\n",
    "dw_mapping={\n",
    "    0: 'Monday', \n",
    "    1: 'Tuesday', \n",
    "    2: 'Wednesday', \n",
    "    3: 'Thursday', \n",
    "    4: 'Friday',\n",
    "    5: 'Saturday', \n",
    "    6: 'Sunday'\n",
    "} \n",
    "df['day_of_week_name']=df['DoB'].dt.weekday.map(dw_mapping)\n",
    "```\n",
    "\n",
    "#### Get the age from the date of birth\n",
    "```python\n",
    "today = pd.to_datetime('today')\n",
    "df['age'] = today.year - df['DoB'].dt.year\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = pd.to_datetime('today')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2017\n",
       "1       2017\n",
       "2       2017\n",
       "3       2016\n",
       "4       2016\n",
       "        ... \n",
       "9795    2017\n",
       "9796    2016\n",
       "9797    2016\n",
       "9798    2016\n",
       "9799    2016\n",
       "Name: order_date, Length: 9800, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       6\n",
       "1       6\n",
       "2       6\n",
       "3       7\n",
       "4       7\n",
       "       ..\n",
       "9795    6\n",
       "9796    7\n",
       "9797    7\n",
       "9798    7\n",
       "9799    7\n",
       "Name: order_date, Length: 9800, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.year - df['order_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        8\n",
       "1        8\n",
       "2       12\n",
       "3       11\n",
       "4       11\n",
       "        ..\n",
       "9795    21\n",
       "9796    12\n",
       "9797    12\n",
       "9798    12\n",
       "9799    12\n",
       "Name: order_date, Length: 9800, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Wednesday\n",
       "1       Wednesday\n",
       "2          Monday\n",
       "3         Tuesday\n",
       "4         Tuesday\n",
       "          ...    \n",
       "9795       Sunday\n",
       "9796      Tuesday\n",
       "9797      Tuesday\n",
       "9798      Tuesday\n",
       "9799      Tuesday\n",
       "Name: order_date, Length: 9800, dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_date'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       November\n",
       "1       November\n",
       "2           June\n",
       "3        October\n",
       "4        October\n",
       "          ...   \n",
       "9795         May\n",
       "9796     January\n",
       "9797     January\n",
       "9798     January\n",
       "9799     January\n",
       "Name: order_date, Length: 9800, dtype: object"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_date'].dt.month_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Column containing only the Order Month\n",
    "By using `Timestamp` objects for dates, a lot of time-related properties are provided by pandas. For example the `month`, but also `year`, `quarter`,… All of these properties are accessible by the dt accessor like `year`, `month`, `day`, `day_of_week`, `day_of_year`, `is_leap_year`, `week`, etc. We can also access methods using `dt` accessor like `day_name()`, `month_name()`, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>segment</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>region</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>sales</th>\n",
       "      <th>order_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date  ship_date       ship_mode customer_id  \\\n",
       "0       1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3       4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     customer_name    segment        country             city       state  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   postal_code region       product_id         category sub_category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        product_name     sales  order_month  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600           11  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400           11  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200            6  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775           10  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680           10  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_month'] = df['order_date'].dt.month\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Delivery Time from Order Date and Ship Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ship_date</th>\n",
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       "      <th>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
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       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
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       "      <td>Claire Gute</td>\n",
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       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date  ship_date       ship_mode customer_id  \\\n",
       "0       1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3       4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     customer_name    segment        country             city       state  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   postal_code region       product_id         category sub_category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        product_name     sales  order_month  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600           11   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400           11   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200            6   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775           10   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680           10   \n",
       "\n",
       "  delivery_time  \n",
       "0        3 days  \n",
       "1        3 days  \n",
       "2        4 days  \n",
       "3        7 days  \n",
       "4        7 days  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['delivery_time'] = df['ship_date'] - df['order_date']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count  Dtype          \n",
      "---  ------         --------------  -----          \n",
      " 0   row_id         9800 non-null   int64          \n",
      " 1   order_id       9800 non-null   object         \n",
      " 2   order_date     9800 non-null   datetime64[ns] \n",
      " 3   ship_date      9800 non-null   datetime64[ns] \n",
      " 4   ship_mode      9800 non-null   object         \n",
      " 5   customer_id    9800 non-null   object         \n",
      " 6   customer_name  9800 non-null   object         \n",
      " 7   segment        9800 non-null   object         \n",
      " 8   country        9800 non-null   object         \n",
      " 9   city           9800 non-null   object         \n",
      " 10  state          9800 non-null   object         \n",
      " 11  postal_code    9789 non-null   float64        \n",
      " 12  region         9800 non-null   object         \n",
      " 13  product_id     9800 non-null   object         \n",
      " 14  category       9800 non-null   object         \n",
      " 15  sub_category   9800 non-null   object         \n",
      " 16  product_name   9800 non-null   object         \n",
      " 17  sales          9800 non-null   float64        \n",
      " 18  order_month    9800 non-null   int64          \n",
      " 19  delivery_time  9800 non-null   timedelta64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(13), timedelta64[ns](1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas.Timedelta 😱\n",
    "\n",
    "Observe the data-type `timedelta64[ns]`. It is nothing but a difference between two dates or times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Column containing Delivery Time in Number of Days\n",
    "\n",
    "`pandas.Timedelta` represents a duration, the difference between two dates or times. Many properties of timedelta can be accessed using `dt` like `components`, `days`, `seconds`, etc. We can also access `timedelta` methods using `dt` accessor like `total_seconds()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
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       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date  ship_date       ship_mode customer_id  \\\n",
       "0       1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3       4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     customer_name    segment        country             city       state  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   postal_code region       product_id         category sub_category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        product_name     sales  order_month  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600           11   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400           11   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200            6   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775           10   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680           10   \n",
       "\n",
       "  delivery_time  \n",
       "0        3 days  \n",
       "1        3 days  \n",
       "2        4 days  \n",
       "3        7 days  \n",
       "4        7 days  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
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       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
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       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
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       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
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       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date  ship_date       ship_mode customer_id  \\\n",
       "0       1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3       4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     customer_name    segment        country             city  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  postal_code  region       product_id         category sub_category  \\\n",
       "0     42420.0   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        product_name     sales  order_month  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600           11   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400           11   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200            6   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775           10   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680           10   \n",
       "\n",
       "   delivery_time delivery_time_days  \n",
       "0         3 days                  3  \n",
       "1         3 days                  3  \n",
       "2         4 days                  4  \n",
       "3         7 days                  7  \n",
       "4         7 days                  7  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['delivery_time_days'] = df['delivery_time'].dt.days\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 21 columns):\n",
      " #   Column              Non-Null Count  Dtype          \n",
      "---  ------              --------------  -----          \n",
      " 0   row_id              9800 non-null   int64          \n",
      " 1   order_id            9800 non-null   object         \n",
      " 2   order_date          9800 non-null   datetime64[ns] \n",
      " 3   ship_date           9800 non-null   datetime64[ns] \n",
      " 4   ship_mode           9800 non-null   object         \n",
      " 5   customer_id         9800 non-null   object         \n",
      " 6   customer_name       9800 non-null   object         \n",
      " 7   segment             9800 non-null   object         \n",
      " 8   country             9800 non-null   object         \n",
      " 9   city                9800 non-null   object         \n",
      " 10  state               9800 non-null   object         \n",
      " 11  postal_code         9789 non-null   float64        \n",
      " 12  region              9800 non-null   object         \n",
      " 13  product_id          9800 non-null   object         \n",
      " 14  category            9800 non-null   object         \n",
      " 15  sub_category        9800 non-null   object         \n",
      " 16  product_name        9800 non-null   object         \n",
      " 17  sales               9800 non-null   float64        \n",
      " 18  order_month         9800 non-null   int64          \n",
      " 19  delivery_time       9800 non-null   timedelta64[ns]\n",
      " 20  delivery_time_days  9800 non-null   int64          \n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(13), timedelta64[ns](1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9798</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9799</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9800 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds\n",
       "0        3      0        0        0             0             0            0\n",
       "1        3      0        0        0             0             0            0\n",
       "2        4      0        0        0             0             0            0\n",
       "3        7      0        0        0             0             0            0\n",
       "4        7      0        0        0             0             0            0\n",
       "...    ...    ...      ...      ...           ...           ...          ...\n",
       "9795     7      0        0        0             0             0            0\n",
       "9796     5      0        0        0             0             0            0\n",
       "9797     5      0        0        0             0             0            0\n",
       "9798     5      0        0        0             0             0            0\n",
       "9799     5      0        0        0             0             0            0\n",
       "\n",
       "[9800 rows x 7 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['delivery_time'].dt.components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       259200.0\n",
       "1       259200.0\n",
       "2       345600.0\n",
       "3       604800.0\n",
       "4       604800.0\n",
       "          ...   \n",
       "9795    604800.0\n",
       "9796    432000.0\n",
       "9797    432000.0\n",
       "9798    432000.0\n",
       "9799    432000.0\n",
       "Name: delivery_time, Length: 9800, dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['delivery_time'].dt.total_seconds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improve Performance by Setting Date Column as the Index\n",
    "```python\n",
    "df = df.set_index(['date'])\n",
    "\n",
    "# Modifying the index inplace\n",
    "df.set_index(['date'], inplace = True)\n",
    "```\n",
    "\n",
    "#### Select data with a specific year and perform aggregation\n",
    "```python\n",
    "# select data with a specific year\n",
    "df.loc['2018']\n",
    "# select data with a specific day\n",
    "df.loc['2018-5-1']\n",
    "# select data using slicing operation\n",
    "df.loc['2018-5-1':'2018-5-5']\n",
    "# Applying aggregation within a date slicing\n",
    "df.loc['2018-5-1':'2018-5-5', ['sales']].mean()\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>CA-2017-152156</td>\n",
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       "      <td>Second Class</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
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       "      <th>4</th>\n",
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       "<p>5 rows × 21 columns</p>\n",
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       "   row_id        order_id order_date  ship_date       ship_mode customer_id  \\\n",
       "0       1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3       4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     customer_name    segment        country             city  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  postal_code  region       product_id         category sub_category  \\\n",
       "0     42420.0   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        product_name     sales  order_month  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600           11   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400           11   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200            6   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775           10   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680           10   \n",
       "\n",
       "   delivery_time delivery_time_days  \n",
       "0         3 days                  3  \n",
       "1         3 days                  3  \n",
       "2         4 days                  4  \n",
       "3         7 days                  7  \n",
       "4         7 days                  7  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>FUR-CH-10000454</td>\n",
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       "      <th>2017-06-12</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
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       "      <td>Darrin Van Huff</td>\n",
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       "      <td>OFF-LA-10000240</td>\n",
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       "      <td>FUR-TA-10000577</td>\n",
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       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
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       "      <td>7</td>\n",
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      "text/plain": [
       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2017-11-08       1  CA-2017-152156 2017-11-11    Second Class    CG-12520   \n",
       "2017-11-08       2  CA-2017-152156 2017-11-11    Second Class    CG-12520   \n",
       "2017-06-12       3  CA-2017-138688 2017-06-16    Second Class    DV-13045   \n",
       "2016-10-11       4  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "2016-10-11       5  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "              customer_name    segment        country             city  \\\n",
       "order_date                                                               \n",
       "2017-11-08      Claire Gute   Consumer  United States        Henderson   \n",
       "2017-11-08      Claire Gute   Consumer  United States        Henderson   \n",
       "2017-06-12  Darrin Van Huff  Corporate  United States      Los Angeles   \n",
       "2016-10-11   Sean O'Donnell   Consumer  United States  Fort Lauderdale   \n",
       "2016-10-11   Sean O'Donnell   Consumer  United States  Fort Lauderdale   \n",
       "\n",
       "                 state  postal_code region       product_id         category  \\\n",
       "order_date                                                                     \n",
       "2017-11-08    Kentucky      42420.0  South  FUR-BO-10001798        Furniture   \n",
       "2017-11-08    Kentucky      42420.0  South  FUR-CH-10000454        Furniture   \n",
       "2017-06-12  California      90036.0   West  OFF-LA-10000240  Office Supplies   \n",
       "2016-10-11     Florida      33311.0  South  FUR-TA-10000577        Furniture   \n",
       "2016-10-11     Florida      33311.0  South  OFF-ST-10000760  Office Supplies   \n",
       "\n",
       "           sub_category                                       product_name  \\\n",
       "order_date                                                                   \n",
       "2017-11-08    Bookcases                  Bush Somerset Collection Bookcase   \n",
       "2017-11-08       Chairs  Hon Deluxe Fabric Upholstered Stacking Chairs,...   \n",
       "2017-06-12       Labels  Self-Adhesive Address Labels for Typewriters b...   \n",
       "2016-10-11       Tables      Bretford CR4500 Series Slim Rectangular Table   \n",
       "2016-10-11      Storage                     Eldon Fold 'N Roll Cart System   \n",
       "\n",
       "               sales  order_month delivery_time  delivery_time_days  \n",
       "order_date                                                           \n",
       "2017-11-08  261.9600           11        3 days                   3  \n",
       "2017-11-08  731.9400           11        3 days                   3  \n",
       "2017-06-12   14.6200            6        4 days                   4  \n",
       "2016-10-11  957.5775           10        7 days                   7  \n",
       "2016-10-11   22.3680           10        7 days                   7  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(['order_date'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>row_id</th>\n",
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       "      <th>ship_date</th>\n",
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       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
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       "      <th>delivery_time</th>\n",
       "      <th>delivery_time_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-11</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-11</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
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       "      <td>Fort Lauderdale</td>\n",
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       "      <td>33311.0</td>\n",
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       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
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       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-22</th>\n",
       "      <td>15</td>\n",
       "      <td>US-2016-118983</td>\n",
       "      <td>2016-11-26</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>HP-14815</td>\n",
       "      <td>Harold Pawlan</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>Texas</td>\n",
       "      <td>76106.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-AP-10002311</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Holmes Replacement Filter for HEPA Air Cleaner...</td>\n",
       "      <td>68.8100</td>\n",
       "      <td>11</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-22</th>\n",
       "      <td>16</td>\n",
       "      <td>US-2016-118983</td>\n",
       "      <td>2016-11-26</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>HP-14815</td>\n",
       "      <td>Harold Pawlan</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Worth</td>\n",
       "      <td>Texas</td>\n",
       "      <td>76106.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-BI-10000756</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Storex DuraTech Recycled Plastic Frosted Binders</td>\n",
       "      <td>2.5440</td>\n",
       "      <td>11</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>25</td>\n",
       "      <td>CA-2016-106320</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EB-13870</td>\n",
       "      <td>Emily Burns</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Orem</td>\n",
       "      <td>Utah</td>\n",
       "      <td>84057.0</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>1044.6300</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>9786</td>\n",
       "      <td>CA-2016-155635</td>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ME-17725</td>\n",
       "      <td>Max Engle</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>40214.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10000962</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco Flexible ACCOHIDE Square Ring Data Binder...</td>\n",
       "      <td>48.8100</td>\n",
       "      <td>5</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>9797</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AR-10001374</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>BIC Brite Liner Highlighters, Chisel Tip</td>\n",
       "      <td>10.3680</td>\n",
       "      <td>1</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>9798</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10004977</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>GE 30524EE4</td>\n",
       "      <td>235.1880</td>\n",
       "      <td>1</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>9799</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10000912</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Anker 24W Portable Micro USB Car Charger</td>\n",
       "      <td>26.3760</td>\n",
       "      <td>1</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>9800</td>\n",
       "      <td>CA-2016-128608</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12490</td>\n",
       "      <td>Cindy Schnelling</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43615.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10000487</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>SanDisk Cruzer 4 GB USB Flash Drive</td>\n",
       "      <td>10.3840</td>\n",
       "      <td>1</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2055 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2016-10-11       4  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "2016-10-11       5  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "2016-11-22      15  US-2016-118983 2016-11-26  Standard Class    HP-14815   \n",
       "2016-11-22      16  US-2016-118983 2016-11-26  Standard Class    HP-14815   \n",
       "2016-09-25      25  CA-2016-106320 2016-09-30  Standard Class    EB-13870   \n",
       "...            ...             ...        ...             ...         ...   \n",
       "2016-05-09    9786  CA-2016-155635 2016-05-13  Standard Class    ME-17725   \n",
       "2016-01-12    9797  CA-2016-128608 2016-01-17  Standard Class    CS-12490   \n",
       "2016-01-12    9798  CA-2016-128608 2016-01-17  Standard Class    CS-12490   \n",
       "2016-01-12    9799  CA-2016-128608 2016-01-17  Standard Class    CS-12490   \n",
       "2016-01-12    9800  CA-2016-128608 2016-01-17  Standard Class    CS-12490   \n",
       "\n",
       "               customer_name      segment        country             city  \\\n",
       "order_date                                                                  \n",
       "2016-10-11    Sean O'Donnell     Consumer  United States  Fort Lauderdale   \n",
       "2016-10-11    Sean O'Donnell     Consumer  United States  Fort Lauderdale   \n",
       "2016-11-22     Harold Pawlan  Home Office  United States       Fort Worth   \n",
       "2016-11-22     Harold Pawlan  Home Office  United States       Fort Worth   \n",
       "2016-09-25       Emily Burns     Consumer  United States             Orem   \n",
       "...                      ...          ...            ...              ...   \n",
       "2016-05-09         Max Engle     Consumer  United States       Louisville   \n",
       "2016-01-12  Cindy Schnelling    Corporate  United States           Toledo   \n",
       "2016-01-12  Cindy Schnelling    Corporate  United States           Toledo   \n",
       "2016-01-12  Cindy Schnelling    Corporate  United States           Toledo   \n",
       "2016-01-12  Cindy Schnelling    Corporate  United States           Toledo   \n",
       "\n",
       "               state  postal_code   region       product_id         category  \\\n",
       "order_date                                                                     \n",
       "2016-10-11   Florida      33311.0    South  FUR-TA-10000577        Furniture   \n",
       "2016-10-11   Florida      33311.0    South  OFF-ST-10000760  Office Supplies   \n",
       "2016-11-22     Texas      76106.0  Central  OFF-AP-10002311  Office Supplies   \n",
       "2016-11-22     Texas      76106.0  Central  OFF-BI-10000756  Office Supplies   \n",
       "2016-09-25      Utah      84057.0     West  FUR-TA-10000577        Furniture   \n",
       "...              ...          ...      ...              ...              ...   \n",
       "2016-05-09  Kentucky      40214.0    South  OFF-BI-10000962  Office Supplies   \n",
       "2016-01-12      Ohio      43615.0     East  OFF-AR-10001374  Office Supplies   \n",
       "2016-01-12      Ohio      43615.0     East  TEC-PH-10004977       Technology   \n",
       "2016-01-12      Ohio      43615.0     East  TEC-PH-10000912       Technology   \n",
       "2016-01-12      Ohio      43615.0     East  TEC-AC-10000487       Technology   \n",
       "\n",
       "           sub_category                                       product_name  \\\n",
       "order_date                                                                   \n",
       "2016-10-11       Tables      Bretford CR4500 Series Slim Rectangular Table   \n",
       "2016-10-11      Storage                     Eldon Fold 'N Roll Cart System   \n",
       "2016-11-22   Appliances  Holmes Replacement Filter for HEPA Air Cleaner...   \n",
       "2016-11-22      Binders   Storex DuraTech Recycled Plastic Frosted Binders   \n",
       "2016-09-25       Tables      Bretford CR4500 Series Slim Rectangular Table   \n",
       "...                 ...                                                ...   \n",
       "2016-05-09      Binders  Acco Flexible ACCOHIDE Square Ring Data Binder...   \n",
       "2016-01-12          Art           BIC Brite Liner Highlighters, Chisel Tip   \n",
       "2016-01-12       Phones                                        GE 30524EE4   \n",
       "2016-01-12       Phones           Anker 24W Portable Micro USB Car Charger   \n",
       "2016-01-12  Accessories                SanDisk Cruzer 4 GB USB Flash Drive   \n",
       "\n",
       "                sales  order_month delivery_time  delivery_time_days  \n",
       "order_date                                                            \n",
       "2016-10-11   957.5775           10        7 days                   7  \n",
       "2016-10-11    22.3680           10        7 days                   7  \n",
       "2016-11-22    68.8100           11        4 days                   4  \n",
       "2016-11-22     2.5440           11        4 days                   4  \n",
       "2016-09-25  1044.6300            9        5 days                   5  \n",
       "...               ...          ...           ...                 ...  \n",
       "2016-05-09    48.8100            5        4 days                   4  \n",
       "2016-01-12    10.3680            1        5 days                   5  \n",
       "2016-01-12   235.1880            1        5 days                   5  \n",
       "2016-01-12    26.3760            1        5 days                   5  \n",
       "2016-01-12    10.3840            1        5 days                   5  \n",
       "\n",
       "[2055 rows x 20 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter Rows based on a year\n",
    "\n",
    "df.loc['2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>order_date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>25</td>\n",
       "      <td>CA-2016-106320</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EB-13870</td>\n",
       "      <td>Emily Burns</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Orem</td>\n",
       "      <td>Utah</td>\n",
       "      <td>84057.0</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>1044.630</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1695</td>\n",
       "      <td>CA-2016-156335</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bayonne</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10002006</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Memorex Micro Travel Drive 16 GB</td>\n",
       "      <td>63.960</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1696</td>\n",
       "      <td>CA-2016-156335</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bayonne</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10003314</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Tuff Stuff Recycled Round Ring Binders</td>\n",
       "      <td>14.460</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1697</td>\n",
       "      <td>CA-2016-156335</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bayonne</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10002726</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>netTALK DUO VoIP Telephone Service</td>\n",
       "      <td>104.980</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>2447</td>\n",
       "      <td>CA-2016-100573</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AM-10705</td>\n",
       "      <td>Anne McFarland</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90004.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-EN-10000461</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>#10- 4 1/8\" x 9 1/2\" Recycled Envelopes</td>\n",
       "      <td>17.480</td>\n",
       "      <td>9</td>\n",
       "      <td>6 days</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>5597</td>\n",
       "      <td>CA-2016-159779</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SB-20185</td>\n",
       "      <td>Sarah Brown</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Concord</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10002735</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>GBC Prestige Therm-A-Bind Covers</td>\n",
       "      <td>68.620</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>5726</td>\n",
       "      <td>CA-2016-103933</td>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>First Class</td>\n",
       "      <td>DR-12880</td>\n",
       "      <td>Dan Reichenbach</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10011.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10004171</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Razer Kraken 7.1 Surround Sound Over Ear USB G...</td>\n",
       "      <td>899.910</td>\n",
       "      <td>9</td>\n",
       "      <td>2 days</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6450</td>\n",
       "      <td>CA-2016-156510</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-13990</td>\n",
       "      <td>Erica Hackney</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Meriden</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>6450.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10000822</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco PRESSTEX Data Binder with Storage Hooks, ...</td>\n",
       "      <td>10.760</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6451</td>\n",
       "      <td>CA-2016-156510</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-13990</td>\n",
       "      <td>Erica Hackney</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Meriden</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>6450.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10002222</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox Color Copier Paper, 11\" x 17\", Ream</td>\n",
       "      <td>45.680</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6452</td>\n",
       "      <td>CA-2016-156510</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-13990</td>\n",
       "      <td>Erica Hackney</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Meriden</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>6450.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AR-10004930</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>Turquoise Lead Holder with Pocket Clip</td>\n",
       "      <td>6.700</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7590</td>\n",
       "      <td>CA-2016-139738</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DK-12895</td>\n",
       "      <td>Dana Kaydos</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Rockford</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>61107.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-AR-10004602</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>Boston KS Multi-Size Manual Pencil Sharpener</td>\n",
       "      <td>128.744</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7837</td>\n",
       "      <td>CA-2016-149083</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-CH-10004289</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Super Steno Chair</td>\n",
       "      <td>307.136</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7838</td>\n",
       "      <td>CA-2016-149083</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10002945</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Permanent Self-Adhesive File Folder Labels for...</td>\n",
       "      <td>12.600</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7839</td>\n",
       "      <td>CA-2016-149083</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-AC-10002567</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Logitech G602 Wireless Gaming Mouse</td>\n",
       "      <td>159.980</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8214</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10001116</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones 1\" Hanging DublLock Ring Binders</td>\n",
       "      <td>6.336</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8215</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-PA-10004665</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Advantus Motivational Note Cards</td>\n",
       "      <td>10.480</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8216</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10004506</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones data.warehouse D-Ring Binders wit...</td>\n",
       "      <td>2.469</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8217</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10001982</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones Custom Binder Spines &amp; Labels</td>\n",
       "      <td>3.264</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>9268</td>\n",
       "      <td>CA-2016-151869</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>CS-11950</td>\n",
       "      <td>Carlos Soltero</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Freeport</td>\n",
       "      <td>New York</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10001545</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Comfortask Task/Swivel Chairs</td>\n",
       "      <td>102.582</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>9269</td>\n",
       "      <td>CA-2016-151869</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>CS-11950</td>\n",
       "      <td>Carlos Soltero</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Freeport</td>\n",
       "      <td>New York</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10002947</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1923</td>\n",
       "      <td>20.040</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>9437</td>\n",
       "      <td>CA-2016-160787</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>BM-11785</td>\n",
       "      <td>Bryan Mills</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19143.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10003712</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco Pressboard Covers with Storage Hooks, 14 ...</td>\n",
       "      <td>2.946</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2016-09-25      25  CA-2016-106320 2016-09-30  Standard Class    EB-13870   \n",
       "2016-09-25    1695  CA-2016-156335 2016-09-28    Second Class    PO-19195   \n",
       "2016-09-25    1696  CA-2016-156335 2016-09-28    Second Class    PO-19195   \n",
       "2016-09-25    1697  CA-2016-156335 2016-09-28    Second Class    PO-19195   \n",
       "2016-09-25    2447  CA-2016-100573 2016-10-01  Standard Class    AM-10705   \n",
       "2016-09-25    5597  CA-2016-159779 2016-09-29  Standard Class    SB-20185   \n",
       "2016-09-25    5726  CA-2016-103933 2016-09-27     First Class    DR-12880   \n",
       "2016-09-25    6450  CA-2016-156510 2016-09-29  Standard Class    EH-13990   \n",
       "2016-09-25    6451  CA-2016-156510 2016-09-29  Standard Class    EH-13990   \n",
       "2016-09-25    6452  CA-2016-156510 2016-09-29  Standard Class    EH-13990   \n",
       "2016-09-25    7590  CA-2016-139738 2016-09-29  Standard Class    DK-12895   \n",
       "2016-09-25    7837  CA-2016-149083 2016-09-30  Standard Class    SH-19975   \n",
       "2016-09-25    7838  CA-2016-149083 2016-09-30  Standard Class    SH-19975   \n",
       "2016-09-25    7839  CA-2016-149083 2016-09-30  Standard Class    SH-19975   \n",
       "2016-09-25    8214  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    8215  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    8216  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    8217  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    9268  CA-2016-151869 2016-09-25        Same Day    CS-11950   \n",
       "2016-09-25    9269  CA-2016-151869 2016-09-25        Same Day    CS-11950   \n",
       "2016-09-25    9437  CA-2016-160787 2016-09-25        Same Day    BM-11785   \n",
       "\n",
       "                  customer_name      segment        country           city  \\\n",
       "order_date                                                                   \n",
       "2016-09-25          Emily Burns     Consumer  United States           Orem   \n",
       "2016-09-25        Phillina Ober  Home Office  United States        Bayonne   \n",
       "2016-09-25        Phillina Ober  Home Office  United States        Bayonne   \n",
       "2016-09-25        Phillina Ober  Home Office  United States        Bayonne   \n",
       "2016-09-25       Anne McFarland     Consumer  United States    Los Angeles   \n",
       "2016-09-25          Sarah Brown     Consumer  United States        Concord   \n",
       "2016-09-25      Dan Reichenbach    Corporate  United States  New York City   \n",
       "2016-09-25        Erica Hackney     Consumer  United States        Meriden   \n",
       "2016-09-25        Erica Hackney     Consumer  United States        Meriden   \n",
       "2016-09-25        Erica Hackney     Consumer  United States        Meriden   \n",
       "2016-09-25          Dana Kaydos     Consumer  United States       Rockford   \n",
       "2016-09-25        Sally Hughsby    Corporate  United States        Seattle   \n",
       "2016-09-25        Sally Hughsby    Corporate  United States        Seattle   \n",
       "2016-09-25        Sally Hughsby    Corporate  United States        Seattle   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25       Carlos Soltero     Consumer  United States       Freeport   \n",
       "2016-09-25       Carlos Soltero     Consumer  United States       Freeport   \n",
       "2016-09-25          Bryan Mills     Consumer  United States   Philadelphia   \n",
       "\n",
       "                    state  postal_code   region       product_id  \\\n",
       "order_date                                                         \n",
       "2016-09-25           Utah      84057.0     West  FUR-TA-10000577   \n",
       "2016-09-25     New Jersey       7002.0     East  TEC-AC-10002006   \n",
       "2016-09-25     New Jersey       7002.0     East  OFF-BI-10003314   \n",
       "2016-09-25     New Jersey       7002.0     East  TEC-PH-10002726   \n",
       "2016-09-25     California      90004.0     West  OFF-EN-10000461   \n",
       "2016-09-25  New Hampshire       3301.0     East  OFF-BI-10002735   \n",
       "2016-09-25       New York      10011.0     East  TEC-AC-10004171   \n",
       "2016-09-25    Connecticut       6450.0     East  OFF-BI-10000822   \n",
       "2016-09-25    Connecticut       6450.0     East  OFF-PA-10002222   \n",
       "2016-09-25    Connecticut       6450.0     East  OFF-AR-10004930   \n",
       "2016-09-25       Illinois      61107.0  Central  OFF-AR-10004602   \n",
       "2016-09-25     Washington      98103.0     West  FUR-CH-10004289   \n",
       "2016-09-25     Washington      98103.0     West  OFF-LA-10002945   \n",
       "2016-09-25     Washington      98103.0     West  TEC-AC-10002567   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-BI-10001116   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-PA-10004665   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-BI-10004506   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-BI-10001982   \n",
       "2016-09-25       New York      11520.0     East  FUR-CH-10001545   \n",
       "2016-09-25       New York      11520.0     East  OFF-PA-10002947   \n",
       "2016-09-25   Pennsylvania      19143.0     East  OFF-BI-10003712   \n",
       "\n",
       "                   category sub_category  \\\n",
       "order_date                                 \n",
       "2016-09-25        Furniture       Tables   \n",
       "2016-09-25       Technology  Accessories   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25       Technology       Phones   \n",
       "2016-09-25  Office Supplies    Envelopes   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25       Technology  Accessories   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies        Paper   \n",
       "2016-09-25  Office Supplies          Art   \n",
       "2016-09-25  Office Supplies          Art   \n",
       "2016-09-25        Furniture       Chairs   \n",
       "2016-09-25  Office Supplies       Labels   \n",
       "2016-09-25       Technology  Accessories   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies        Paper   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25        Furniture       Chairs   \n",
       "2016-09-25  Office Supplies        Paper   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "\n",
       "                                                 product_name     sales  \\\n",
       "order_date                                                                \n",
       "2016-09-25      Bretford CR4500 Series Slim Rectangular Table  1044.630   \n",
       "2016-09-25                   Memorex Micro Travel Drive 16 GB    63.960   \n",
       "2016-09-25             Tuff Stuff Recycled Round Ring Binders    14.460   \n",
       "2016-09-25                 netTALK DUO VoIP Telephone Service   104.980   \n",
       "2016-09-25            #10- 4 1/8\" x 9 1/2\" Recycled Envelopes    17.480   \n",
       "2016-09-25                   GBC Prestige Therm-A-Bind Covers    68.620   \n",
       "2016-09-25  Razer Kraken 7.1 Surround Sound Over Ear USB G...   899.910   \n",
       "2016-09-25  Acco PRESSTEX Data Binder with Storage Hooks, ...    10.760   \n",
       "2016-09-25          Xerox Color Copier Paper, 11\" x 17\", Ream    45.680   \n",
       "2016-09-25             Turquoise Lead Holder with Pocket Clip     6.700   \n",
       "2016-09-25       Boston KS Multi-Size Manual Pencil Sharpener   128.744   \n",
       "2016-09-25                           Global Super Steno Chair   307.136   \n",
       "2016-09-25  Permanent Self-Adhesive File Folder Labels for...    12.600   \n",
       "2016-09-25                Logitech G602 Wireless Gaming Mouse   159.980   \n",
       "2016-09-25      Wilson Jones 1\" Hanging DublLock Ring Binders     6.336   \n",
       "2016-09-25                   Advantus Motivational Note Cards    10.480   \n",
       "2016-09-25  Wilson Jones data.warehouse D-Ring Binders wit...     2.469   \n",
       "2016-09-25         Wilson Jones Custom Binder Spines & Labels     3.264   \n",
       "2016-09-25                  Hon Comfortask Task/Swivel Chairs   102.582   \n",
       "2016-09-25                                         Xerox 1923    20.040   \n",
       "2016-09-25  Acco Pressboard Covers with Storage Hooks, 14 ...     2.946   \n",
       "\n",
       "            order_month delivery_time  delivery_time_days  \n",
       "order_date                                                 \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        3 days                   3  \n",
       "2016-09-25            9        3 days                   3  \n",
       "2016-09-25            9        3 days                   3  \n",
       "2016-09-25            9        6 days                   6  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        2 days                   2  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        0 days                   0  \n",
       "2016-09-25            9        0 days                   0  \n",
       "2016-09-25            9        0 days                   0  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter a specific date\n",
    "\n",
    "df.loc['2016-09-25']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>segment</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>region</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>sales</th>\n",
       "      <th>order_month</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_time_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>25</td>\n",
       "      <td>CA-2016-106320</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EB-13870</td>\n",
       "      <td>Emily Burns</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Orem</td>\n",
       "      <td>Utah</td>\n",
       "      <td>84057.0</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>1044.630</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>281</td>\n",
       "      <td>US-2016-161991</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SC-20725</td>\n",
       "      <td>Steven Cartwright</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>77070.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-BI-10004967</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Round Ring Binders</td>\n",
       "      <td>2.080</td>\n",
       "      <td>9</td>\n",
       "      <td>2 days</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>282</td>\n",
       "      <td>US-2016-161991</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SC-20725</td>\n",
       "      <td>Steven Cartwright</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>77070.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>TEC-PH-10001760</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Bose SoundLink Bluetooth Speaker</td>\n",
       "      <td>1114.400</td>\n",
       "      <td>9</td>\n",
       "      <td>2 days</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>284</td>\n",
       "      <td>CA-2016-130883</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TB-21520</td>\n",
       "      <td>Tracy Blumstein</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>97206.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10000474</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Easy-staple paper</td>\n",
       "      <td>141.760</td>\n",
       "      <td>9</td>\n",
       "      <td>6 days</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>285</td>\n",
       "      <td>CA-2016-130883</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TB-21520</td>\n",
       "      <td>Tracy Blumstein</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>97206.0</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-AC-10001956</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Microsoft Arc Touch Mouse</td>\n",
       "      <td>239.800</td>\n",
       "      <td>9</td>\n",
       "      <td>6 days</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>286</td>\n",
       "      <td>CA-2016-130883</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TB-21520</td>\n",
       "      <td>Tracy Blumstein</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>97206.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10004100</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 216</td>\n",
       "      <td>31.104</td>\n",
       "      <td>9</td>\n",
       "      <td>6 days</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1420</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10000501</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Petty Cash Envelope</td>\n",
       "      <td>86.272</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1421</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-BI-10000778</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>GBC VeloBinder Electric Binding Machine</td>\n",
       "      <td>72.588</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1422</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-AP-10004980</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>3M Replacement Filter for Office Air Cleaner f...</td>\n",
       "      <td>60.672</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1423</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-BI-10003984</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Lock-Up Easel 'Spel-Binder'</td>\n",
       "      <td>77.031</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1424</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-ST-10000798</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>2300 Heavy-Duty Transfer File Systems by Perma</td>\n",
       "      <td>119.904</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1425</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-PH-10001750</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Samsung Rugby III</td>\n",
       "      <td>263.960</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1426</td>\n",
       "      <td>CA-2016-124800</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RW-19540</td>\n",
       "      <td>Rick Wilson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>85204.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-ST-10002743</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>SAFCO Boltless Steel Shelving</td>\n",
       "      <td>363.648</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1649</td>\n",
       "      <td>CA-2016-120341</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20200</td>\n",
       "      <td>Sarah Foster</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19143.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10004224</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Catalog Binders with Expanding Posts</td>\n",
       "      <td>121.104</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1650</td>\n",
       "      <td>CA-2016-120341</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20200</td>\n",
       "      <td>Sarah Foster</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19143.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10003357</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Grandstream GXP2100 Mainstream Business Phone</td>\n",
       "      <td>45.894</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1695</td>\n",
       "      <td>CA-2016-156335</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bayonne</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10002006</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Memorex Micro Travel Drive 16 GB</td>\n",
       "      <td>63.960</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1696</td>\n",
       "      <td>CA-2016-156335</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bayonne</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10003314</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Tuff Stuff Recycled Round Ring Binders</td>\n",
       "      <td>14.460</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1697</td>\n",
       "      <td>CA-2016-156335</td>\n",
       "      <td>2016-09-28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bayonne</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10002726</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>netTALK DUO VoIP Telephone Service</td>\n",
       "      <td>104.980</td>\n",
       "      <td>9</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>2447</td>\n",
       "      <td>CA-2016-100573</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AM-10705</td>\n",
       "      <td>Anne McFarland</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90004.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-EN-10000461</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>#10- 4 1/8\" x 9 1/2\" Recycled Envelopes</td>\n",
       "      <td>17.480</td>\n",
       "      <td>9</td>\n",
       "      <td>6 days</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>2497</td>\n",
       "      <td>CA-2016-122371</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BS-11800</td>\n",
       "      <td>Bryan Spruell</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>92037.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-ST-10002370</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Sortfiler Multipurpose Personal File Organizer...</td>\n",
       "      <td>64.170</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>2498</td>\n",
       "      <td>CA-2016-122371</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BS-11800</td>\n",
       "      <td>Bryan Spruell</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>92037.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-EN-10000056</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>Cameo Buff Policy Envelopes</td>\n",
       "      <td>124.460</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>2556</td>\n",
       "      <td>CA-2016-111458</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PF-19165</td>\n",
       "      <td>Philip Fox</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10035.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10001590</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Dell Slim USB Multimedia Keyboard</td>\n",
       "      <td>50.000</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>5118</td>\n",
       "      <td>CA-2016-127173</td>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GM-14500</td>\n",
       "      <td>Gene McClure</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Oceanside</td>\n",
       "      <td>New York</td>\n",
       "      <td>11572.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-FA-10004854</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Vinyl Coated Wire Paper Clips in Organizer Box...</td>\n",
       "      <td>34.440</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>5119</td>\n",
       "      <td>CA-2016-127173</td>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GM-14500</td>\n",
       "      <td>Gene McClure</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Oceanside</td>\n",
       "      <td>New York</td>\n",
       "      <td>11572.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-MA-10002859</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Machines</td>\n",
       "      <td>Ativa MDM8000 8-Sheet Micro-Cut Shredder</td>\n",
       "      <td>629.930</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>5120</td>\n",
       "      <td>CA-2016-127173</td>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GM-14500</td>\n",
       "      <td>Gene McClure</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Oceanside</td>\n",
       "      <td>New York</td>\n",
       "      <td>11572.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10000088</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>GBC Imprintable Covers</td>\n",
       "      <td>79.056</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>5597</td>\n",
       "      <td>CA-2016-159779</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SB-20185</td>\n",
       "      <td>Sarah Brown</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Concord</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10002735</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>GBC Prestige Therm-A-Bind Covers</td>\n",
       "      <td>68.620</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>5726</td>\n",
       "      <td>CA-2016-103933</td>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>First Class</td>\n",
       "      <td>DR-12880</td>\n",
       "      <td>Dan Reichenbach</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10011.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10004171</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Razer Kraken 7.1 Surround Sound Over Ear USB G...</td>\n",
       "      <td>899.910</td>\n",
       "      <td>9</td>\n",
       "      <td>2 days</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6450</td>\n",
       "      <td>CA-2016-156510</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-13990</td>\n",
       "      <td>Erica Hackney</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Meriden</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>6450.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10000822</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco PRESSTEX Data Binder with Storage Hooks, ...</td>\n",
       "      <td>10.760</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6451</td>\n",
       "      <td>CA-2016-156510</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-13990</td>\n",
       "      <td>Erica Hackney</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Meriden</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>6450.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10002222</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox Color Copier Paper, 11\" x 17\", Ream</td>\n",
       "      <td>45.680</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6452</td>\n",
       "      <td>CA-2016-156510</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EH-13990</td>\n",
       "      <td>Erica Hackney</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Meriden</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>6450.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AR-10004930</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>Turquoise Lead Holder with Pocket Clip</td>\n",
       "      <td>6.700</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7590</td>\n",
       "      <td>CA-2016-139738</td>\n",
       "      <td>2016-09-29</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DK-12895</td>\n",
       "      <td>Dana Kaydos</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Rockford</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>61107.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-AR-10004602</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>Boston KS Multi-Size Manual Pencil Sharpener</td>\n",
       "      <td>128.744</td>\n",
       "      <td>9</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7837</td>\n",
       "      <td>CA-2016-149083</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-CH-10004289</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Super Steno Chair</td>\n",
       "      <td>307.136</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7838</td>\n",
       "      <td>CA-2016-149083</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10002945</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Permanent Self-Adhesive File Folder Labels for...</td>\n",
       "      <td>12.600</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>7839</td>\n",
       "      <td>CA-2016-149083</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SH-19975</td>\n",
       "      <td>Sally Hughsby</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-AC-10002567</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Logitech G602 Wireless Gaming Mouse</td>\n",
       "      <td>159.980</td>\n",
       "      <td>9</td>\n",
       "      <td>5 days</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8214</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10001116</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones 1\" Hanging DublLock Ring Binders</td>\n",
       "      <td>6.336</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8215</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-PA-10004665</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Advantus Motivational Note Cards</td>\n",
       "      <td>10.480</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8216</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10004506</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones data.warehouse D-Ring Binders wit...</td>\n",
       "      <td>2.469</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>8217</td>\n",
       "      <td>CA-2016-120845</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ML-17395</td>\n",
       "      <td>Marina Lichtenstein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37130.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-BI-10001982</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones Custom Binder Spines &amp; Labels</td>\n",
       "      <td>3.264</td>\n",
       "      <td>9</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>9268</td>\n",
       "      <td>CA-2016-151869</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>CS-11950</td>\n",
       "      <td>Carlos Soltero</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Freeport</td>\n",
       "      <td>New York</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10001545</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Comfortask Task/Swivel Chairs</td>\n",
       "      <td>102.582</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>9269</td>\n",
       "      <td>CA-2016-151869</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>CS-11950</td>\n",
       "      <td>Carlos Soltero</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Freeport</td>\n",
       "      <td>New York</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10002947</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1923</td>\n",
       "      <td>20.040</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>9437</td>\n",
       "      <td>CA-2016-160787</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>BM-11785</td>\n",
       "      <td>Bryan Mills</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19143.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10003712</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco Pressboard Covers with Storage Hooks, 14 ...</td>\n",
       "      <td>2.946</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2016-09-25      25  CA-2016-106320 2016-09-30  Standard Class    EB-13870   \n",
       "2016-09-26     281  US-2016-161991 2016-09-28    Second Class    SC-20725   \n",
       "2016-09-26     282  US-2016-161991 2016-09-28    Second Class    SC-20725   \n",
       "2016-09-26     284  CA-2016-130883 2016-10-02  Standard Class    TB-21520   \n",
       "2016-09-26     285  CA-2016-130883 2016-10-02  Standard Class    TB-21520   \n",
       "2016-09-26     286  CA-2016-130883 2016-10-02  Standard Class    TB-21520   \n",
       "2016-09-26    1420  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1421  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1422  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1423  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1424  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1425  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1426  CA-2016-124800 2016-09-30  Standard Class    RW-19540   \n",
       "2016-09-26    1649  CA-2016-120341 2016-09-29    Second Class    SF-20200   \n",
       "2016-09-26    1650  CA-2016-120341 2016-09-29    Second Class    SF-20200   \n",
       "2016-09-25    1695  CA-2016-156335 2016-09-28    Second Class    PO-19195   \n",
       "2016-09-25    1696  CA-2016-156335 2016-09-28    Second Class    PO-19195   \n",
       "2016-09-25    1697  CA-2016-156335 2016-09-28    Second Class    PO-19195   \n",
       "2016-09-25    2447  CA-2016-100573 2016-10-01  Standard Class    AM-10705   \n",
       "2016-09-26    2497  CA-2016-122371 2016-10-01  Standard Class    BS-11800   \n",
       "2016-09-26    2498  CA-2016-122371 2016-10-01  Standard Class    BS-11800   \n",
       "2016-09-26    2556  CA-2016-111458 2016-09-30  Standard Class    PF-19165   \n",
       "2016-09-26    5118  CA-2016-127173 2016-10-03  Standard Class    GM-14500   \n",
       "2016-09-26    5119  CA-2016-127173 2016-10-03  Standard Class    GM-14500   \n",
       "2016-09-26    5120  CA-2016-127173 2016-10-03  Standard Class    GM-14500   \n",
       "2016-09-25    5597  CA-2016-159779 2016-09-29  Standard Class    SB-20185   \n",
       "2016-09-25    5726  CA-2016-103933 2016-09-27     First Class    DR-12880   \n",
       "2016-09-25    6450  CA-2016-156510 2016-09-29  Standard Class    EH-13990   \n",
       "2016-09-25    6451  CA-2016-156510 2016-09-29  Standard Class    EH-13990   \n",
       "2016-09-25    6452  CA-2016-156510 2016-09-29  Standard Class    EH-13990   \n",
       "2016-09-25    7590  CA-2016-139738 2016-09-29  Standard Class    DK-12895   \n",
       "2016-09-25    7837  CA-2016-149083 2016-09-30  Standard Class    SH-19975   \n",
       "2016-09-25    7838  CA-2016-149083 2016-09-30  Standard Class    SH-19975   \n",
       "2016-09-25    7839  CA-2016-149083 2016-09-30  Standard Class    SH-19975   \n",
       "2016-09-25    8214  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    8215  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    8216  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    8217  CA-2016-120845 2016-10-02  Standard Class    ML-17395   \n",
       "2016-09-25    9268  CA-2016-151869 2016-09-25        Same Day    CS-11950   \n",
       "2016-09-25    9269  CA-2016-151869 2016-09-25        Same Day    CS-11950   \n",
       "2016-09-25    9437  CA-2016-160787 2016-09-25        Same Day    BM-11785   \n",
       "\n",
       "                  customer_name      segment        country           city  \\\n",
       "order_date                                                                   \n",
       "2016-09-25          Emily Burns     Consumer  United States           Orem   \n",
       "2016-09-26    Steven Cartwright     Consumer  United States        Houston   \n",
       "2016-09-26    Steven Cartwright     Consumer  United States        Houston   \n",
       "2016-09-26      Tracy Blumstein     Consumer  United States       Portland   \n",
       "2016-09-26      Tracy Blumstein     Consumer  United States       Portland   \n",
       "2016-09-26      Tracy Blumstein     Consumer  United States       Portland   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26          Rick Wilson    Corporate  United States           Mesa   \n",
       "2016-09-26         Sarah Foster     Consumer  United States   Philadelphia   \n",
       "2016-09-26         Sarah Foster     Consumer  United States   Philadelphia   \n",
       "2016-09-25        Phillina Ober  Home Office  United States        Bayonne   \n",
       "2016-09-25        Phillina Ober  Home Office  United States        Bayonne   \n",
       "2016-09-25        Phillina Ober  Home Office  United States        Bayonne   \n",
       "2016-09-25       Anne McFarland     Consumer  United States    Los Angeles   \n",
       "2016-09-26        Bryan Spruell  Home Office  United States      San Diego   \n",
       "2016-09-26        Bryan Spruell  Home Office  United States      San Diego   \n",
       "2016-09-26           Philip Fox     Consumer  United States  New York City   \n",
       "2016-09-26         Gene McClure     Consumer  United States      Oceanside   \n",
       "2016-09-26         Gene McClure     Consumer  United States      Oceanside   \n",
       "2016-09-26         Gene McClure     Consumer  United States      Oceanside   \n",
       "2016-09-25          Sarah Brown     Consumer  United States        Concord   \n",
       "2016-09-25      Dan Reichenbach    Corporate  United States  New York City   \n",
       "2016-09-25        Erica Hackney     Consumer  United States        Meriden   \n",
       "2016-09-25        Erica Hackney     Consumer  United States        Meriden   \n",
       "2016-09-25        Erica Hackney     Consumer  United States        Meriden   \n",
       "2016-09-25          Dana Kaydos     Consumer  United States       Rockford   \n",
       "2016-09-25        Sally Hughsby    Corporate  United States        Seattle   \n",
       "2016-09-25        Sally Hughsby    Corporate  United States        Seattle   \n",
       "2016-09-25        Sally Hughsby    Corporate  United States        Seattle   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25  Marina Lichtenstein    Corporate  United States   Murfreesboro   \n",
       "2016-09-25       Carlos Soltero     Consumer  United States       Freeport   \n",
       "2016-09-25       Carlos Soltero     Consumer  United States       Freeport   \n",
       "2016-09-25          Bryan Mills     Consumer  United States   Philadelphia   \n",
       "\n",
       "                    state  postal_code   region       product_id  \\\n",
       "order_date                                                         \n",
       "2016-09-25           Utah      84057.0     West  FUR-TA-10000577   \n",
       "2016-09-26          Texas      77070.0  Central  OFF-BI-10004967   \n",
       "2016-09-26          Texas      77070.0  Central  TEC-PH-10001760   \n",
       "2016-09-26         Oregon      97206.0     West  OFF-PA-10000474   \n",
       "2016-09-26         Oregon      97206.0     West  TEC-AC-10001956   \n",
       "2016-09-26         Oregon      97206.0     West  OFF-PA-10004100   \n",
       "2016-09-26        Arizona      85204.0     West  OFF-PA-10000501   \n",
       "2016-09-26        Arizona      85204.0     West  OFF-BI-10000778   \n",
       "2016-09-26        Arizona      85204.0     West  OFF-AP-10004980   \n",
       "2016-09-26        Arizona      85204.0     West  OFF-BI-10003984   \n",
       "2016-09-26        Arizona      85204.0     West  OFF-ST-10000798   \n",
       "2016-09-26        Arizona      85204.0     West  TEC-PH-10001750   \n",
       "2016-09-26        Arizona      85204.0     West  OFF-ST-10002743   \n",
       "2016-09-26   Pennsylvania      19143.0     East  OFF-BI-10004224   \n",
       "2016-09-26   Pennsylvania      19143.0     East  TEC-PH-10003357   \n",
       "2016-09-25     New Jersey       7002.0     East  TEC-AC-10002006   \n",
       "2016-09-25     New Jersey       7002.0     East  OFF-BI-10003314   \n",
       "2016-09-25     New Jersey       7002.0     East  TEC-PH-10002726   \n",
       "2016-09-25     California      90004.0     West  OFF-EN-10000461   \n",
       "2016-09-26     California      92037.0     West  OFF-ST-10002370   \n",
       "2016-09-26     California      92037.0     West  OFF-EN-10000056   \n",
       "2016-09-26       New York      10035.0     East  TEC-AC-10001590   \n",
       "2016-09-26       New York      11572.0     East  OFF-FA-10004854   \n",
       "2016-09-26       New York      11572.0     East  TEC-MA-10002859   \n",
       "2016-09-26       New York      11572.0     East  OFF-BI-10000088   \n",
       "2016-09-25  New Hampshire       3301.0     East  OFF-BI-10002735   \n",
       "2016-09-25       New York      10011.0     East  TEC-AC-10004171   \n",
       "2016-09-25    Connecticut       6450.0     East  OFF-BI-10000822   \n",
       "2016-09-25    Connecticut       6450.0     East  OFF-PA-10002222   \n",
       "2016-09-25    Connecticut       6450.0     East  OFF-AR-10004930   \n",
       "2016-09-25       Illinois      61107.0  Central  OFF-AR-10004602   \n",
       "2016-09-25     Washington      98103.0     West  FUR-CH-10004289   \n",
       "2016-09-25     Washington      98103.0     West  OFF-LA-10002945   \n",
       "2016-09-25     Washington      98103.0     West  TEC-AC-10002567   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-BI-10001116   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-PA-10004665   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-BI-10004506   \n",
       "2016-09-25      Tennessee      37130.0    South  OFF-BI-10001982   \n",
       "2016-09-25       New York      11520.0     East  FUR-CH-10001545   \n",
       "2016-09-25       New York      11520.0     East  OFF-PA-10002947   \n",
       "2016-09-25   Pennsylvania      19143.0     East  OFF-BI-10003712   \n",
       "\n",
       "                   category sub_category  \\\n",
       "order_date                                 \n",
       "2016-09-25        Furniture       Tables   \n",
       "2016-09-26  Office Supplies      Binders   \n",
       "2016-09-26       Technology       Phones   \n",
       "2016-09-26  Office Supplies        Paper   \n",
       "2016-09-26       Technology  Accessories   \n",
       "2016-09-26  Office Supplies        Paper   \n",
       "2016-09-26  Office Supplies        Paper   \n",
       "2016-09-26  Office Supplies      Binders   \n",
       "2016-09-26  Office Supplies   Appliances   \n",
       "2016-09-26  Office Supplies      Binders   \n",
       "2016-09-26  Office Supplies      Storage   \n",
       "2016-09-26       Technology       Phones   \n",
       "2016-09-26  Office Supplies      Storage   \n",
       "2016-09-26  Office Supplies      Binders   \n",
       "2016-09-26       Technology       Phones   \n",
       "2016-09-25       Technology  Accessories   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25       Technology       Phones   \n",
       "2016-09-25  Office Supplies    Envelopes   \n",
       "2016-09-26  Office Supplies      Storage   \n",
       "2016-09-26  Office Supplies    Envelopes   \n",
       "2016-09-26       Technology  Accessories   \n",
       "2016-09-26  Office Supplies    Fasteners   \n",
       "2016-09-26       Technology     Machines   \n",
       "2016-09-26  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25       Technology  Accessories   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies        Paper   \n",
       "2016-09-25  Office Supplies          Art   \n",
       "2016-09-25  Office Supplies          Art   \n",
       "2016-09-25        Furniture       Chairs   \n",
       "2016-09-25  Office Supplies       Labels   \n",
       "2016-09-25       Technology  Accessories   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies        Paper   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "2016-09-25        Furniture       Chairs   \n",
       "2016-09-25  Office Supplies        Paper   \n",
       "2016-09-25  Office Supplies      Binders   \n",
       "\n",
       "                                                 product_name     sales  \\\n",
       "order_date                                                                \n",
       "2016-09-25      Bretford CR4500 Series Slim Rectangular Table  1044.630   \n",
       "2016-09-26                                 Round Ring Binders     2.080   \n",
       "2016-09-26                   Bose SoundLink Bluetooth Speaker  1114.400   \n",
       "2016-09-26                                  Easy-staple paper   141.760   \n",
       "2016-09-26                          Microsoft Arc Touch Mouse   239.800   \n",
       "2016-09-26                                          Xerox 216    31.104   \n",
       "2016-09-26                                Petty Cash Envelope    86.272   \n",
       "2016-09-26            GBC VeloBinder Electric Binding Machine    72.588   \n",
       "2016-09-26  3M Replacement Filter for Office Air Cleaner f...    60.672   \n",
       "2016-09-26                        Lock-Up Easel 'Spel-Binder'    77.031   \n",
       "2016-09-26     2300 Heavy-Duty Transfer File Systems by Perma   119.904   \n",
       "2016-09-26                                  Samsung Rugby III   263.960   \n",
       "2016-09-26                      SAFCO Boltless Steel Shelving   363.648   \n",
       "2016-09-26               Catalog Binders with Expanding Posts   121.104   \n",
       "2016-09-26      Grandstream GXP2100 Mainstream Business Phone    45.894   \n",
       "2016-09-25                   Memorex Micro Travel Drive 16 GB    63.960   \n",
       "2016-09-25             Tuff Stuff Recycled Round Ring Binders    14.460   \n",
       "2016-09-25                 netTALK DUO VoIP Telephone Service   104.980   \n",
       "2016-09-25            #10- 4 1/8\" x 9 1/2\" Recycled Envelopes    17.480   \n",
       "2016-09-26  Sortfiler Multipurpose Personal File Organizer...    64.170   \n",
       "2016-09-26                        Cameo Buff Policy Envelopes   124.460   \n",
       "2016-09-26                  Dell Slim USB Multimedia Keyboard    50.000   \n",
       "2016-09-26  Vinyl Coated Wire Paper Clips in Organizer Box...    34.440   \n",
       "2016-09-26           Ativa MDM8000 8-Sheet Micro-Cut Shredder   629.930   \n",
       "2016-09-26                             GBC Imprintable Covers    79.056   \n",
       "2016-09-25                   GBC Prestige Therm-A-Bind Covers    68.620   \n",
       "2016-09-25  Razer Kraken 7.1 Surround Sound Over Ear USB G...   899.910   \n",
       "2016-09-25  Acco PRESSTEX Data Binder with Storage Hooks, ...    10.760   \n",
       "2016-09-25          Xerox Color Copier Paper, 11\" x 17\", Ream    45.680   \n",
       "2016-09-25             Turquoise Lead Holder with Pocket Clip     6.700   \n",
       "2016-09-25       Boston KS Multi-Size Manual Pencil Sharpener   128.744   \n",
       "2016-09-25                           Global Super Steno Chair   307.136   \n",
       "2016-09-25  Permanent Self-Adhesive File Folder Labels for...    12.600   \n",
       "2016-09-25                Logitech G602 Wireless Gaming Mouse   159.980   \n",
       "2016-09-25      Wilson Jones 1\" Hanging DublLock Ring Binders     6.336   \n",
       "2016-09-25                   Advantus Motivational Note Cards    10.480   \n",
       "2016-09-25  Wilson Jones data.warehouse D-Ring Binders wit...     2.469   \n",
       "2016-09-25         Wilson Jones Custom Binder Spines & Labels     3.264   \n",
       "2016-09-25                  Hon Comfortask Task/Swivel Chairs   102.582   \n",
       "2016-09-25                                         Xerox 1923    20.040   \n",
       "2016-09-25  Acco Pressboard Covers with Storage Hooks, 14 ...     2.946   \n",
       "\n",
       "            order_month delivery_time  delivery_time_days  \n",
       "order_date                                                 \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-26            9        2 days                   2  \n",
       "2016-09-26            9        2 days                   2  \n",
       "2016-09-26            9        6 days                   6  \n",
       "2016-09-26            9        6 days                   6  \n",
       "2016-09-26            9        6 days                   6  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        3 days                   3  \n",
       "2016-09-26            9        3 days                   3  \n",
       "2016-09-25            9        3 days                   3  \n",
       "2016-09-25            9        3 days                   3  \n",
       "2016-09-25            9        3 days                   3  \n",
       "2016-09-25            9        6 days                   6  \n",
       "2016-09-26            9        5 days                   5  \n",
       "2016-09-26            9        5 days                   5  \n",
       "2016-09-26            9        4 days                   4  \n",
       "2016-09-26            9        7 days                   7  \n",
       "2016-09-26            9        7 days                   7  \n",
       "2016-09-26            9        7 days                   7  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        2 days                   2  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        4 days                   4  \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        5 days                   5  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        7 days                   7  \n",
       "2016-09-25            9        0 days                   0  \n",
       "2016-09-25            9        0 days                   0  \n",
       "2016-09-25            9        0 days                   0  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows based on date slicing\n",
    "\n",
    "df.loc['2016-09-25':'2016-09-26']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>1044.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>2.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>1114.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>141.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>239.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>31.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>86.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>72.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>60.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>77.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>119.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>263.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>363.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>121.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>45.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>63.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>14.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>104.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>17.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>64.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>124.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>34.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>629.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>79.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>68.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>899.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>10.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>45.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>128.744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>307.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>12.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>159.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>6.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>10.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>2.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>3.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>102.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>20.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-25</th>\n",
       "      <td>2.946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               sales\n",
       "order_date          \n",
       "2016-09-25  1044.630\n",
       "2016-09-26     2.080\n",
       "2016-09-26  1114.400\n",
       "2016-09-26   141.760\n",
       "2016-09-26   239.800\n",
       "2016-09-26    31.104\n",
       "2016-09-26    86.272\n",
       "2016-09-26    72.588\n",
       "2016-09-26    60.672\n",
       "2016-09-26    77.031\n",
       "2016-09-26   119.904\n",
       "2016-09-26   263.960\n",
       "2016-09-26   363.648\n",
       "2016-09-26   121.104\n",
       "2016-09-26    45.894\n",
       "2016-09-25    63.960\n",
       "2016-09-25    14.460\n",
       "2016-09-25   104.980\n",
       "2016-09-25    17.480\n",
       "2016-09-26    64.170\n",
       "2016-09-26   124.460\n",
       "2016-09-26    50.000\n",
       "2016-09-26    34.440\n",
       "2016-09-26   629.930\n",
       "2016-09-26    79.056\n",
       "2016-09-25    68.620\n",
       "2016-09-25   899.910\n",
       "2016-09-25    10.760\n",
       "2016-09-25    45.680\n",
       "2016-09-25     6.700\n",
       "2016-09-25   128.744\n",
       "2016-09-25   307.136\n",
       "2016-09-25    12.600\n",
       "2016-09-25   159.980\n",
       "2016-09-25     6.336\n",
       "2016-09-25    10.480\n",
       "2016-09-25     2.469\n",
       "2016-09-25     3.264\n",
       "2016-09-25   102.582\n",
       "2016-09-25    20.040\n",
       "2016-09-25     2.946"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2016-09-25':'2016-09-26', ['sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Sales Amount:\n",
      " sales    2.08\n",
      "dtype: float64\n",
      "Max Sales Amount:\n",
      " sales    1114.4\n",
      "dtype: float64\n",
      "Mean Sales Amount:\n",
      " sales    164.78122\n",
      "dtype: float64\n",
      "Spread Sales Amount:\n",
      " sales    272.046464\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Applying aggregation within a date slicing\n",
    "\n",
    "print(\"Min Sales Amount:\\n\", df.loc['2016-09-25':'2016-09-26', ['sales']].min())\n",
    "print(\"Max Sales Amount:\\n\", df.loc['2016-09-25':'2016-09-26', ['sales']].max())\n",
    "print(\"Mean Sales Amount:\\n\", df.loc['2016-09-25':'2016-09-26', ['sales']].mean())\n",
    "print(\"Spread Sales Amount:\\n\", df.loc['2016-09-25':'2016-09-26', ['sales']].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting Data Based on Index vs Values and Resetting Index\n",
    "```python\n",
    "df.sort_index(ascending = False)\n",
    "df.sort_values(by = 'sales')\n",
    "df.reset_index()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>segment</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>region</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>sales</th>\n",
       "      <th>order_month</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_time_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-08</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-08</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-12</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-11</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-11</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2017-11-08       1  CA-2017-152156 2017-11-11    Second Class    CG-12520   \n",
       "2017-11-08       2  CA-2017-152156 2017-11-11    Second Class    CG-12520   \n",
       "2017-06-12       3  CA-2017-138688 2017-06-16    Second Class    DV-13045   \n",
       "2016-10-11       4  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "2016-10-11       5  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "              customer_name    segment        country             city  \\\n",
       "order_date                                                               \n",
       "2017-11-08      Claire Gute   Consumer  United States        Henderson   \n",
       "2017-11-08      Claire Gute   Consumer  United States        Henderson   \n",
       "2017-06-12  Darrin Van Huff  Corporate  United States      Los Angeles   \n",
       "2016-10-11   Sean O'Donnell   Consumer  United States  Fort Lauderdale   \n",
       "2016-10-11   Sean O'Donnell   Consumer  United States  Fort Lauderdale   \n",
       "\n",
       "                 state  postal_code region       product_id         category  \\\n",
       "order_date                                                                     \n",
       "2017-11-08    Kentucky      42420.0  South  FUR-BO-10001798        Furniture   \n",
       "2017-11-08    Kentucky      42420.0  South  FUR-CH-10000454        Furniture   \n",
       "2017-06-12  California      90036.0   West  OFF-LA-10000240  Office Supplies   \n",
       "2016-10-11     Florida      33311.0  South  FUR-TA-10000577        Furniture   \n",
       "2016-10-11     Florida      33311.0  South  OFF-ST-10000760  Office Supplies   \n",
       "\n",
       "           sub_category                                       product_name  \\\n",
       "order_date                                                                   \n",
       "2017-11-08    Bookcases                  Bush Somerset Collection Bookcase   \n",
       "2017-11-08       Chairs  Hon Deluxe Fabric Upholstered Stacking Chairs,...   \n",
       "2017-06-12       Labels  Self-Adhesive Address Labels for Typewriters b...   \n",
       "2016-10-11       Tables      Bretford CR4500 Series Slim Rectangular Table   \n",
       "2016-10-11      Storage                     Eldon Fold 'N Roll Cart System   \n",
       "\n",
       "               sales  order_month delivery_time  delivery_time_days  \n",
       "order_date                                                           \n",
       "2017-11-08  261.9600           11        3 days                   3  \n",
       "2017-11-08  731.9400           11        3 days                   3  \n",
       "2017-06-12   14.6200            6        4 days                   4  \n",
       "2016-10-11  957.5775           10        7 days                   7  \n",
       "2016-10-11   22.3680           10        7 days                   7  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
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    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>order_date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>646</td>\n",
       "      <td>CA-2018-126221</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CC-12430</td>\n",
       "      <td>Chuck Clark</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>47201.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-AP-10002457</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Eureka The Boss Plus 12-Amp Hard Box Upright V...</td>\n",
       "      <td>209.300</td>\n",
       "      <td>12</td>\n",
       "      <td>6 days</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
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       "      <td>CA-2018-156720</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Standard Class</td>\n",
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       "      <td>Jill Matthias</td>\n",
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       "      <td>Loveland</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>80538.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-FA-10003472</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Bagged Rubber Bands</td>\n",
       "      <td>3.024</td>\n",
       "      <td>12</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>909</td>\n",
       "      <td>CA-2018-143259</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>Patrick O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
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       "      <td>10009.0</td>\n",
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       "      <td>OFF-BI-10003684</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Wilson Jones Legal Size Ring Binders</td>\n",
       "      <td>52.776</td>\n",
       "      <td>12</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
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       "      <td>Standard Class</td>\n",
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       "      <td>Patrick O'Donnell</td>\n",
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       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>10009.0</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10004774</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Gear Head AU3700S Headset</td>\n",
       "      <td>90.930</td>\n",
       "      <td>12</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
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       "      <th>2018-12-30</th>\n",
       "      <td>907</td>\n",
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       "      <td>2019-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PO-18865</td>\n",
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       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
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       "      <td>12</td>\n",
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       "      <td>4</td>\n",
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       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2018-12-30     646  CA-2018-126221 2019-01-05  Standard Class    CC-12430   \n",
       "2018-12-30    5092  CA-2018-156720 2019-01-03  Standard Class    JM-15580   \n",
       "2018-12-30     909  CA-2018-143259 2019-01-03  Standard Class    PO-18865   \n",
       "2018-12-30     908  CA-2018-143259 2019-01-03  Standard Class    PO-18865   \n",
       "2018-12-30     907  CA-2018-143259 2019-01-03  Standard Class    PO-18865   \n",
       "\n",
       "                customer_name      segment        country           city  \\\n",
       "order_date                                                                 \n",
       "2018-12-30        Chuck Clark  Home Office  United States       Columbus   \n",
       "2018-12-30      Jill Matthias     Consumer  United States       Loveland   \n",
       "2018-12-30  Patrick O'Donnell     Consumer  United States  New York City   \n",
       "2018-12-30  Patrick O'Donnell     Consumer  United States  New York City   \n",
       "2018-12-30  Patrick O'Donnell     Consumer  United States  New York City   \n",
       "\n",
       "               state  postal_code   region       product_id         category  \\\n",
       "order_date                                                                     \n",
       "2018-12-30   Indiana      47201.0  Central  OFF-AP-10002457  Office Supplies   \n",
       "2018-12-30  Colorado      80538.0     West  OFF-FA-10003472  Office Supplies   \n",
       "2018-12-30  New York      10009.0     East  OFF-BI-10003684  Office Supplies   \n",
       "2018-12-30  New York      10009.0     East  TEC-PH-10004774       Technology   \n",
       "2018-12-30  New York      10009.0     East  FUR-BO-10003441        Furniture   \n",
       "\n",
       "           sub_category                                       product_name  \\\n",
       "order_date                                                                   \n",
       "2018-12-30   Appliances  Eureka The Boss Plus 12-Amp Hard Box Upright V...   \n",
       "2018-12-30    Fasteners                                Bagged Rubber Bands   \n",
       "2018-12-30      Binders               Wilson Jones Legal Size Ring Binders   \n",
       "2018-12-30       Phones                          Gear Head AU3700S Headset   \n",
       "2018-12-30    Bookcases  Bush Westfield Collection Bookcases, Fully Ass...   \n",
       "\n",
       "              sales  order_month delivery_time  delivery_time_days  \n",
       "order_date                                                          \n",
       "2018-12-30  209.300           12        6 days                   6  \n",
       "2018-12-30    3.024           12        4 days                   4  \n",
       "2018-12-30   52.776           12        4 days                   4  \n",
       "2018-12-30   90.930           12        4 days                   4  \n",
       "2018-12-30  323.136           12        4 days                   4  "
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       "      <th>2018-06-19</th>\n",
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       "      <td>77095.0</td>\n",
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       "      <td>OFF-AP-10002906</td>\n",
       "      <td>Office Supplies</td>\n",
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       "      <th>2018-03-02</th>\n",
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       "      <td>Roland Schwarz</td>\n",
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       "      <td>Waco</td>\n",
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       "      <td>76706.0</td>\n",
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       "      <td>OFF-BI-10004022</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco Suede Grain Vinyl Round Ring Binder</td>\n",
       "      <td>0.556</td>\n",
       "      <td>3</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-21</th>\n",
       "      <td>8659</td>\n",
       "      <td>CA-2017-168361</td>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>KB-16600</td>\n",
       "      <td>Ken Brennan</td>\n",
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       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>60623.0</td>\n",
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       "      <td>OFF-BI-10003727</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Avery Durable Slant Ring Binders With Label Ho...</td>\n",
       "      <td>0.836</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>4712</td>\n",
       "      <td>CA-2015-112403</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>JO-15280</td>\n",
       "      <td>Jas O'Carroll</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19120.0</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-BI-10003529</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Avery Round Ring Poly Binders</td>\n",
       "      <td>0.852</td>\n",
       "      <td>3</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
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       "      <th>2015-09-26</th>\n",
       "      <td>2107</td>\n",
       "      <td>US-2015-152723</td>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>HG-14965</td>\n",
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       "      <td>Mesquite</td>\n",
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       "      <td>75150.0</td>\n",
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       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco 3-Hole Punch</td>\n",
       "      <td>0.876</td>\n",
       "      <td>9</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "            row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "order_date                                                                  \n",
       "2018-06-19    4102  US-2018-102288 2018-06-23  Standard Class    ZC-21910   \n",
       "2018-03-02    9293  CA-2018-124114 2018-03-02        Same Day    RS-19765   \n",
       "2017-06-21    8659  CA-2017-168361 2017-06-25  Standard Class    KB-16600   \n",
       "2015-03-31    4712  CA-2015-112403 2015-03-31        Same Day    JO-15280   \n",
       "2015-09-26    2107  US-2015-152723 2015-09-26        Same Day    HG-14965   \n",
       "\n",
       "               customer_name    segment        country          city  \\\n",
       "order_date                                                             \n",
       "2018-06-19  Zuschuss Carroll   Consumer  United States       Houston   \n",
       "2018-03-02    Roland Schwarz  Corporate  United States          Waco   \n",
       "2017-06-21       Ken Brennan  Corporate  United States       Chicago   \n",
       "2015-03-31     Jas O'Carroll   Consumer  United States  Philadelphia   \n",
       "2015-09-26     Henry Goldwyn  Corporate  United States      Mesquite   \n",
       "\n",
       "                   state  postal_code   region       product_id  \\\n",
       "order_date                                                        \n",
       "2018-06-19         Texas      77095.0  Central  OFF-AP-10002906   \n",
       "2018-03-02         Texas      76706.0  Central  OFF-BI-10004022   \n",
       "2017-06-21      Illinois      60623.0  Central  OFF-BI-10003727   \n",
       "2015-03-31  Pennsylvania      19120.0     East  OFF-BI-10003529   \n",
       "2015-09-26         Texas      75150.0  Central  OFF-BI-10003460   \n",
       "\n",
       "                   category sub_category  \\\n",
       "order_date                                 \n",
       "2018-06-19  Office Supplies   Appliances   \n",
       "2018-03-02  Office Supplies      Binders   \n",
       "2017-06-21  Office Supplies      Binders   \n",
       "2015-03-31  Office Supplies      Binders   \n",
       "2015-09-26  Office Supplies      Binders   \n",
       "\n",
       "                                                 product_name  sales  \\\n",
       "order_date                                                             \n",
       "2018-06-19  Hoover Replacement Belt for Commercial Guardsm...  0.444   \n",
       "2018-03-02           Acco Suede Grain Vinyl Round Ring Binder  0.556   \n",
       "2017-06-21  Avery Durable Slant Ring Binders With Label Ho...  0.836   \n",
       "2015-03-31                      Avery Round Ring Poly Binders  0.852   \n",
       "2015-09-26                                  Acco 3-Hole Punch  0.876   \n",
       "\n",
       "            order_month delivery_time  delivery_time_days  \n",
       "order_date                                                 \n",
       "2018-06-19            6        4 days                   4  \n",
       "2018-03-02            3        0 days                   0  \n",
       "2017-06-21            6        4 days                   4  \n",
       "2015-03-31            3        0 days                   0  \n",
       "2015-09-26            9        0 days                   0  "
      ]
     },
     "execution_count": 206,
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    "df.sort_values(by = 'sales').head()"
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>segment</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>...</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>region</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>product_name</th>\n",
       "      <th>sales</th>\n",
       "      <th>order_month</th>\n",
       "      <th>delivery_time</th>\n",
       "      <th>delivery_time_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>11</td>\n",
       "      <td>3 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>6</td>\n",
       "      <td>4 days</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>10</td>\n",
       "      <td>7 days</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date  row_id        order_id  ship_date       ship_mode customer_id  \\\n",
       "0 2017-11-08       1  CA-2017-152156 2017-11-11    Second Class    CG-12520   \n",
       "1 2017-11-08       2  CA-2017-152156 2017-11-11    Second Class    CG-12520   \n",
       "2 2017-06-12       3  CA-2017-138688 2017-06-16    Second Class    DV-13045   \n",
       "3 2016-10-11       4  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "4 2016-10-11       5  US-2016-108966 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     customer_name    segment        country             city  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  postal_code  region       product_id         category sub_category  \\\n",
       "0     42420.0   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        product_name     sales  order_month  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600           11   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400           11   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200            6   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775           10   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680           10   \n",
       "\n",
       "   delivery_time delivery_time_days  \n",
       "0         3 days                  3  \n",
       "1         3 days                  3  \n",
       "2         4 days                  4  \n",
       "3         7 days                  7  \n",
       "4         7 days                  7  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
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    "## Summary\n",
    "`pandas` is well suited for many different kinds of data:\n",
    "> Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet  \n",
    "> Ordered and unordered (not necessarily fixed-frequency) time series data.  \n",
    "> Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels  \n",
    "> Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure  \n",
    "\n",
    "Here are just a few of the things that pandas does well:\n",
    "> Easy **handling of missing data (represented as NaN)** in floating point as well as non-floating point data  \n",
    "> **Size mutability**: columns can be inserted and deleted from DataFrame and higher dimensional objects  \n",
    "> Automatic and explicit **data alignment**: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations  \n",
    "> Powerful, flexible **group by** functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data  \n",
    "> Make it **easy to convert** ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects  \n",
    "> Intelligent label-based **slicing, fancy indexing, and subsetting** of large data sets  \n",
    "> Intuitive **merging and joining** data sets  \n",
    "> Flexible **reshaping and pivoting** of data sets  \n",
    "> **Hierarchical labeling** of axes (possible to have multiple labels per tick)  \n",
    "> Robust IO tools for loading data from **flat files** (CSV and delimited), **Excel files, databases**, and saving / loading data from the **ultrafast HDF5 format**  \n",
    "> **Time series-specific functionality**: date range generation and frequency conversion, moving window statistics, date shifting, and lagging.  \n"
   ]
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